From 221bc3e19bc09b389430e417ba4263a0e19adc0f Mon Sep 17 00:00:00 2001
From: Kerem Keptig <kerem.keptig@stud-mail.uni-wuerzburg.de>
Date: Mon, 10 Mar 2025 19:35:43 +0100
Subject: [PATCH] Delete sp_high_school.ipynb

---
 sp_high_school.ipynb | 856 -------------------------------------------
 1 file changed, 856 deletions(-)
 delete mode 100644 sp_high_school.ipynb

diff --git a/sp_high_school.ipynb b/sp_high_school.ipynb
deleted file mode 100644
index 6b14464..0000000
--- a/sp_high_school.ipynb
+++ /dev/null
@@ -1,856 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pathpyG as pp\n",
-    "from pathpyG.algorithms.components import connected_components\n",
-    "from pathpyG.algorithms.temporal import lift_order_temporal\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import networkx as nx\n",
-    "from collections import defaultdict\n",
-    "from itertools import product\n",
-    "from collections import Counter\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess(dataset_name: str):\n",
-    "    \"\"\"\n",
-    "    read the original data file and return the DataFrame that has columns ['u', 'i', 'ts', 'label', 'idx']\n",
-    "    :param dataset_name: str, dataset name\n",
-    "    :return:\n",
-    "    \"\"\"\n",
-    "    u_list, i_list, ts_list, label_list = [], [], [], []\n",
-    "    feat_l = []\n",
-    "    idx_list = []\n",
-    "\n",
-    "    with open(dataset_name) as f:\n",
-    "        # skip the first line\n",
-    "        s = next(f)\n",
-    "        previous_time = -1\n",
-    "        for idx, line in enumerate(f):\n",
-    "            e = line.strip().split(',')\n",
-    "            # user_id\n",
-    "            u = int(e[0])\n",
-    "            # item_id\n",
-    "            i = int(e[1])\n",
-    "\n",
-    "            # timestamp\n",
-    "            ts = float(e[2])\n",
-    "            # check whether time in ascending order\n",
-    "\n",
-    "            u_list.append(u)\n",
-    "            i_list.append(i)\n",
-    "            ts_list.append(ts)\n",
-    "            \n",
-    "            # edge index\n",
-    "            idx_list.append(idx)\n",
-    "\n",
-    "            \n",
-    "    return pd.DataFrame({'u': u_list,\n",
-    "                         'i': i_list,\n",
-    "                         'ts': ts_list,\n",
-    "                         'idx': idx_list})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for wikipedia dataset indexed correctly, so no need to use this\n",
-    "def reindex(df: pd.DataFrame, bipartite: bool = True):\n",
-    "    new_df = df.copy()\n",
-    "    if bipartite:\n",
-    "        upper_u = df.u.max() + 1\n",
-    "        new_i = df.i + upper_u\n",
-    "        new_df.i = new_i\n",
-    "    new_df.u += 1\n",
-    "    new_df.i += 1\n",
-    "    new_df.idx += 1\n",
-    "    return new_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class BipartiteTemporalPercolation:\n",
-    "\n",
-    "    def __init__(self, temporal_graph, total_events):\n",
-    "        self.temporal_graph = temporal_graph\n",
-    "        self.total_events = total_events\n",
-    "        # for count underlying temporal nodes\n",
-    "        self.src_array = torch.tensor([edge[0] for edge in self.temporal_graph.edges], dtype=torch.long)\n",
-    "        self.dst_array = torch.tensor([edge[1] for edge in self.temporal_graph.edges], dtype=torch.long)\n",
-    "\n",
-    "    def create_event_graph(self, delta_t):\n",
-    "        event_edge_index = pp.algorithms.temporal.lift_order_temporal(self.temporal_graph, delta=delta_t)\n",
-    "        event_graph = pp.Graph.from_edge_index(event_edge_index)\n",
-    "\n",
-    "        return event_graph\n",
-    "    \n",
-    "    # count the number of underlying temporal nodes, S_G\n",
-    "    def count_underlying_temporal_nodes(self, largest_indices):\n",
-    "\n",
-    "        if len(largest_indices) > 0:\n",
-    "            sel_src = self.src_array[largest_indices]\n",
-    "            sel_dst = self.dst_array[largest_indices]\n",
-    "            unique_nodes = np.unique(np.concatenate([sel_src, sel_dst]))\n",
-    "        else:\n",
-    "            unique_nodes = 0\n",
-    "        \n",
-    "        return unique_nodes.size \n",
-    "    \n",
-    "    # Lifetime of the event-graph component, S_LT\n",
-    "    def compute_component_lifetime(self, largest_indices):\n",
-    "\n",
-    "        if len(largest_indices) > 0:\n",
-    "            times = [float(self.temporal_graph.data.time[i]) for i in largest_indices]\n",
-    "            lifetime  = max(times) - min(times)\n",
-    "        else:\n",
-    "            lifetime  = 0.0  \n",
-    "\n",
-    "        return lifetime      \n",
-    "\n",
-    "    def percolation_analysis(self, delta_t_values):\n",
-    "        results = {}\n",
-    "        reachability = {}\n",
-    "        survivability = {}\n",
-    "        percolation_metrics = {}\n",
-    "        \n",
-    "        for delta_t in delta_t_values:\n",
-    "            print(f\"Processing δ={delta_t}...\")\n",
-    "            try:\n",
-    "                event_graph = self.create_event_graph(delta_t)\n",
-    "                num_components, labels = pp.algorithms.components.connected_components(event_graph)\n",
-    "                \n",
-    "                # component labels, the value at that index tells us which connected component that node belongs to\n",
-    "                labels_tensor = torch.tensor(labels)\n",
-    "                uniques, counts = torch.unique(labels_tensor, return_counts=True)\n",
-    "\n",
-    "                # I also store the component sizes because for XE calculation I need to extract\n",
-    "                # all component sizes except largest one\n",
-    "                # [1,1,1,2,2,3,3,3] -> [3, 2, 3]\n",
-    "                # it turns into a dictionary {3: 2, 2: 1}, component sizes, and their counts\n",
-    "                component_size_counts = dict(Counter(counts.tolist())) \n",
-    "\n",
-    "                if counts.numel() > 0:\n",
-    "                    largest_component_size = counts.max().item()\n",
-    "                    average_component_size = counts.float().mean().item()\n",
-    "                else:\n",
-    "                    largest_component_size = 0\n",
-    "                    average_component_size = 0\n",
-    "                \n",
-    "                # label of largest CC\n",
-    "                if len(uniques) > 0:\n",
-    "                    largest_label = uniques[counts.argmax()].item()\n",
-    "                else:\n",
-    "                    largest_label = -1\n",
-    "                \n",
-    "                # - True (1), the node belongs to the LCC\n",
-    "                # - False (0), it belongs to another component\n",
-    "                largest_indices = (labels_tensor == largest_label).nonzero()\n",
-    "\n",
-    "                # the result to a 1D tensor\n",
-    "                # it returns a 2D tensor [[3], [7], [12]]\n",
-    "                # reshapes it into a flat 1D tensor [3, 7, 12]\n",
-    "                largest_indices = largest_indices.view(-1)\n",
-    "\n",
-    "                # This list contains the indices of all event-graph nodes that belong to the LCC.\n",
-    "                largest_indices = largest_indices.tolist()  # Example output: [3, 7, 12]\n",
-    "\n",
-    "                S_E = len(largest_indices)\n",
-    "                \n",
-    "                # Per-component node sizes and lifetimes\n",
-    "                node_component_sizes = {}\n",
-    "                component_lifetimes = {}\n",
-    "\n",
-    "                # same logic as above, but this time I iterate over unique labels to count manually\n",
-    "                for label in uniques.tolist():\n",
-    "                    indices = (labels_tensor == label).nonzero().view(-1).tolist()\n",
-    "\n",
-    "                    # Compute S_G (number of unique nodes)\n",
-    "                    node_count = self.count_underlying_temporal_nodes(indices)\n",
-    "                    node_component_sizes[node_count] = node_component_sizes.get(node_count, 0) + 1\n",
-    "\n",
-    "                    # Compute S_LT (lifetime of each component)\n",
-    "                    component_times = [self.temporal_graph.data.time[i].item() for i in indices]\n",
-    "                    if component_times:\n",
-    "                        t_min = min(component_times)\n",
-    "                        t_max = max(component_times)\n",
-    "                        component_lifetime = t_max - t_min\n",
-    "                        component_lifetimes[component_lifetime] = component_lifetimes.get(component_lifetime, 0) + 1\n",
-    "\n",
-    "                S_G = self.count_underlying_temporal_nodes(largest_indices)\n",
-    "\n",
-    "                S_LT = self.compute_component_lifetime(largest_indices)\n",
-    "                \n",
-    "                total_components = len(uniques)\n",
-    "                # Reachability and survivability\n",
-    "                reachable_events = largest_component_size / self.temporal_graph.n\n",
-    "                if event_graph.n > 0 and event_graph.m > 0: # T\n",
-    "                    component_times = [self.temporal_graph.data.time[i].item() for i in event_graph.data.edge_index[0]]\n",
-    "                    max_time = max(component_times)\n",
-    "                    min_time = min(component_times)\n",
-    "                    survivable_events = (max_time - min_time) / (self.temporal_graph.data.time.max().item() - self.temporal_graph.data.time.min().item())\n",
-    "\n",
-    "                # from Directed percolation paper\n",
-    "                percolation_metrics[delta_t] = {\n",
-    "                    \"M\": counts.float().mean().item(),  # Mean cluster mass\n",
-    "                    \"V\": len(labels) / num_components if num_components > 0 else 0,  # Mean spatial volume\n",
-    "                    \"T\": survivable_events,  # Temporal range ratio\n",
-    "                }\n",
-    "               \n",
-    "                \n",
-    "                results[delta_t] = {\n",
-    "                    \"largest_component_size\": largest_component_size,\n",
-    "                    \"total_components\": total_components,\n",
-    "                    \"average_component_size\": average_component_size,\n",
-    "                    \"component_sizes\": component_size_counts,\n",
-    "                    \"node_component_sizes\": node_component_sizes,\n",
-    "                    \"component_lifetimes\": component_lifetimes,\n",
-    "                    \"S_E\": S_E,\n",
-    "                    \"S_G\": S_G,\n",
-    "                    \"S_LT\": S_LT\n",
-    "                }\n",
-    "                \n",
-    "                reachability[delta_t] = reachable_events\n",
-    "                survivability[delta_t] = survivable_events\n",
-    "            \n",
-    "            except Exception as e:\n",
-    "                print(f\"Error at δ={delta_t}: {e}\")\n",
-    "                results[delta_t] = {\n",
-    "                    \"largest_component_size\": 0,\n",
-    "                    \"total_components\": 0,\n",
-    "                    \"average_component_size\": 0,\n",
-    "                    \"component_sizes\": 0,\n",
-    "                    \"node_component_sizes\": 0,\n",
-    "                    \"component_lifetimes\": 0,\n",
-    "                    \"S_E\": 0,\n",
-    "                    \"S_G\": 0,\n",
-    "                    \"S_LT\": 0,\n",
-    "                    \"S_E\": 0,\n",
-    "                    \"S_G\": 0,\n",
-    "                    \"S_LT\": 0\n",
-    "                }\n",
-    "                reachability[delta_t] = 0\n",
-    "                survivability[delta_t] = 0\n",
-    "                percolation_metrics[delta_t] = {\"M\": 0, \"V\": 0, \"T\": 0}\n",
-    "        \n",
-    "        return results, reachability, percolation_metrics\n",
-    "\n",
-    "\n",
-    "    def find_critical_threshold(self, analysis_results, delta_t_values):\n",
-    "            \"\"\"\n",
-    "            Identify the critical δt where the largest connected component (LCC) rapidly grows.\n",
-    "            \"\"\"\n",
-    "            \n",
-    "            num_event_graph_nodes = np.array([result[\"S_E\"] for result in analysis_results.values()])\n",
-    "\n",
-    "            if len(delta_t_values) < 2:\n",
-    "                raise ValueError(\"Not enough data points to determine the critical threshold.\")\n",
-    "\n",
-    "            susceptibility = np.diff(num_event_graph_nodes) / np.diff(delta_t_values)\n",
-    "\n",
-    "            # I added because it made an error in the length of the susceptibility\n",
-    "            # so, the first and last values are removed in the derivative calculation\n",
-    "            midpoints = (delta_t_values[:-1] + delta_t_values[1:]) / 2\n",
-    "\n",
-    "            critical_index = np.argmax(susceptibility)\n",
-    "            critical_dt = midpoints[critical_index]\n",
-    "\n",
-    "            return critical_dt\n",
-    "\n",
-    "    def finite_size_scaling(self, analysis_results, delta_t_values, sizes, critical_delta_t, beta=0.5, gamma=2/3):\n",
-    "        \"\"\"\n",
-    "        X-axis: N^(1/2) * (δt - δt_c)\n",
-    "        Y-axis: N^(β/2) * ρ_E(δt - δt_c)\n",
-    "        \"\"\"\n",
-    "        scaled_results = {}\n",
-    "        total_n = self.temporal_graph.n\n",
-    "        for size in sizes:\n",
-    "            largest_components = np.array([analysis_results[dt][\"largest_component_size\"] for dt in delta_t_values])\n",
-    "    \n",
-    "            # (ρ_E = S/N) # N* is the maximum possible value that S* can get as a single component\n",
-    "            rho_E = largest_components / total_n\n",
-    "\n",
-    "            # N^(1/2) * (δt - δt_c)\n",
-    "            scaled_delta_t = (delta_t_values - critical_delta_t) * (size ** (1 / 2))\n",
-    "\n",
-    "            # ρ_E(δt - δt_c) * N^(β/2) \n",
-    "            scaled_largest =  rho_E * (size ** (beta / 2))\n",
-    "\n",
-    "            scaled_results[size] = {\n",
-    "                \"scaled_delta_t\": scaled_delta_t,\n",
-    "                \"scaled_largest_component\": scaled_largest,\n",
-    "            }\n",
-    "\n",
-    "        return scaled_results\n",
-    "\n",
-    "    def plot_finite_size_scaling(self, scaled_results, use_log_scale=False):\n",
-    "        \"\"\"\n",
-    "        Plots finite-size scaling results to check for data collapse.\n",
-    "\n",
-    "        Parameters:\n",
-    "        - scaled_results: Dictionary containing scaled data for different sizes.\n",
-    "        - use_log_scale: If True, applies a log-scale transformation to the x-axis.\n",
-    "        \"\"\"\n",
-    "        plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "        for size, data in scaled_results.items():\n",
-    "            scaled_delta_t = np.array(data[\"scaled_delta_t\"])\n",
-    "            scaled_largest_component = np.array(data[\"scaled_largest_component\"])\n",
-    "            \n",
-    "            if use_log_scale:\n",
-    "                scaled_delta_t = np.sign(scaled_delta_t) * np.log1p(np.abs(scaled_delta_t))\n",
-    "\n",
-    "            plt.plot(scaled_delta_t, scaled_largest_component, label=f\"Size {size}\", marker=\"o\", markersize=5, linestyle=\"-\")\n",
-    "\n",
-    "        plt.axvline(0, color=\"r\", linestyle=\"--\", linewidth=2, label=r\"Critical $\\delta t_c$\")\n",
-    "        plt.xlabel(r\"Scaled $\\delta t$ $\\left(N^{1/2} (\\delta t - \\delta t_c)\\right)$ (log-scale)\" if use_log_scale else \n",
-    "                r\"Scaled $\\delta t$ $\\left(N^{1/2} (\\delta t - \\delta t_c)\\right)$\")\n",
-    "        plt.ylabel(r\"Scaled Largest Component $\\left(N^{\\beta/2} \\rho_E(\\delta t - \\delta t_c)\\right)$\")\n",
-    "        \n",
-    "        plt.title(\"Finite Size Scaling Collapse (Log-Scale)\" if use_log_scale else \"Finite Size Scaling Collapse\")\n",
-    "\n",
-    "        plt.legend(loc=\"best\", frameon=True)\n",
-    "        plt.grid(True, linestyle=\"--\", alpha=0.6)\n",
-    "        plt.show()\n",
-    "\n",
-    "    def optimize_scaling_exponents(self, analysis_results, delta_t_values, sizes, critical_delta_t, beta_range, nu_range):\n",
-    "        \"\"\"\n",
-    "        power-law to optimize β and ν for the best scaling collapse by minimizing variance.\n",
-    "        \"\"\"\n",
-    "        best_beta, best_nu = None, None\n",
-    "        best_score = float(\"inf\")\n",
-    "\n",
-    "        for beta, nu in product(beta_range, nu_range):\n",
-    "            scaled_results = self.finite_size_scaling(analysis_results, delta_t_values, sizes, critical_delta_t, beta, nu)\n",
-    "            variances = [np.var(data[\"scaled_largest_component\"]) for data in scaled_results.values()]\n",
-    "            score = np.mean(variances)\n",
-    "            \n",
-    "            if score < best_score:\n",
-    "                best_score = score\n",
-    "                best_beta, best_nu = beta, nu\n",
-    "\n",
-    "        return best_beta, best_nu\n",
-    "\n",
-    "    def plot_survivability(self, survivability):\n",
-    "        delta_t_values = list(survivability.keys())\n",
-    "        survivability_values = list(survivability.values())\n",
-    "\n",
-    "        plt.figure(figsize=(10, 6))\n",
-    "        plt.plot(delta_t_values, survivability_values, marker=\"o\", linestyle=\"-\", label=\"Survivability\")\n",
-    "        plt.xlabel(\"δt (time threshold)\")\n",
-    "        plt.ylabel(\"Survivability\")\n",
-    "        plt.title(\"Survivability vs δt\")\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.show()\n",
-    "        \n",
-    "    def plot_reachability(self, reachability):\n",
-    "        delta_t_values = list(reachability.keys())\n",
-    "        reachability_values = list(reachability.values())\n",
-    "\n",
-    "        plt.figure(figsize=(10, 6))\n",
-    "        plt.plot(delta_t_values, reachability_values, marker=\"o\", linestyle=\"-\", label=\"Reachability\")\n",
-    "        plt.xlabel(\"δt (time threshold)\")\n",
-    "        plt.ylabel(\"Reachability\")\n",
-    "        plt.title(\"Reachability vs δt\")\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.show()\n",
-    "\n",
-    "\n",
-    "    def plot_metrics(self, delta_t_values, percolation_metrics):\n",
-    "\n",
-    "        # Filter out delta_t values with missing or None metrics\n",
-    "        valid_delta_t = [\n",
-    "            delta_t for delta_t in delta_t_values \n",
-    "            if delta_t in percolation_metrics \n",
-    "            and percolation_metrics[delta_t] is not None\n",
-    "            and all(percolation_metrics[delta_t].get(k) is not None for k in [\"M\", \"V\", \"T\"])\n",
-    "        ]\n",
-    "        \n",
-    "        # Extract metrics for valid delta_t values\n",
-    "        M_values = [percolation_metrics[delta_t][\"M\"] for delta_t in valid_delta_t]\n",
-    "        V_values = [percolation_metrics[delta_t][\"V\"] for delta_t in valid_delta_t]\n",
-    "        T_values = [percolation_metrics[delta_t][\"T\"] for delta_t in valid_delta_t]\n",
-    "\n",
-    "        # Plot Mean Cluster Mass (M)\n",
-    "        plt.figure(figsize=(8, 6))\n",
-    "        plt.plot(valid_delta_t, M_values, marker=\"o\", label=\"Mean Cluster Mass (M)\")\n",
-    "        plt.xlabel(\"Delta t\")\n",
-    "        plt.ylabel(\"M\")\n",
-    "        plt.title(\"Mean Cluster Mass vs Delta t\")\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.show()\n",
-    "\n",
-    "        # Plot Mean Spatial Volume (V)\n",
-    "        plt.figure(figsize=(8, 6))\n",
-    "        plt.plot(valid_delta_t, V_values, marker=\"o\", label=\"Mean Spatial Volume (V)\", color=\"g\")\n",
-    "        plt.xlabel(\"Delta t\")\n",
-    "        plt.ylabel(\"V\")\n",
-    "        plt.title(\"Mean Spatial Volume vs Delta t\")\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.show()\n",
-    "\n",
-    "        # Plot Mean Survival Time (T)\n",
-    "        plt.figure(figsize=(8, 6))\n",
-    "        plt.plot(valid_delta_t, T_values, marker=\"o\", label=\"Mean Survival Time (T)\", color=\"r\")\n",
-    "        plt.xlabel(\"Delta t\")\n",
-    "        plt.ylabel(\"T\")\n",
-    "        plt.title(\"Mean Survival Time vs Delta t\")\n",
-    "        plt.grid(True)\n",
-    "        plt.legend()\n",
-    "        plt.show()\n",
-    "    \n",
-    "    def compare_component_measures(self, results):\n",
-    "            \"\"\"Compare S_E, S_G, and S_LT by plotting them across delta_t values.\"\"\"\n",
-    "            df = pd.DataFrame.from_dict(results, orient='index')\n",
-    "            df = df[['S_E', 'S_G', 'S_LT']]\n",
-    "            df = (df - df.min()) / (df.max() - df.min())  # Normalize for fair comparison\n",
-    "            \n",
-    "            seconds_per_day = 24 * 3600\n",
-    "            df.index = df.index.astype(float) / seconds_per_day\n",
-    "\n",
-    "            plt.figure(figsize=(10, 6))\n",
-    "            plt.plot(df.index, df['S_E'], marker='o', label='S_E (Event Graph Nodes)')\n",
-    "            plt.plot(df.index , df['S_G'], marker='s', label='S_G (Underlying Graph Nodes)')\n",
-    "            plt.plot(df.index, df['S_LT'], marker='^', label='S_LT (Component Lifetime)')\n",
-    "            \n",
-    "            plt.xlabel(\"δ_t (days)\")\n",
-    "            plt.ylabel(\"Normalized Component Size\")\n",
-    "            plt.title(\"Comparison of Different Component Size Measures\")\n",
-    "            plt.legend()\n",
-    "            plt.grid(True)\n",
-    "            plt.show()\n",
-    "            \n",
-    "            # Compute correlation matrix\n",
-    "            correlation_matrix = df.corr()\n",
-    "            print(\"\\nCorrelation between measures:\")\n",
-    "            print(correlation_matrix)\n",
-    "\n",
-    "    def plot_largest_component(self, results, delta_t_values):\n",
-    "            df = pd.DataFrame.from_dict(results, orient='index')\n",
-    "            df = df[['S_E']]\n",
-    "            num_event_graph_nodes = df[\"S_E\"]\n",
-    "\n",
-    "            seconds_per_hour =  3600\n",
-    "            delta_t_values = delta_t_values / seconds_per_hour\n",
-    "            \n",
-    "            critical_delta_t = self.find_critical_threshold(results, delta_t_values)\n",
-    "            \n",
-    "            plt.figure(figsize=(10, 6))\n",
-    "            plt.plot(delta_t_values, num_event_graph_nodes, label=\"Number of Events\", marker=\"o\")\n",
-    "            plt.axvline(x=critical_delta_t, color=\"r\", linestyle=\"--\", label=f\"Critical δt={critical_delta_t:.2f} hours\")\n",
-    "            \n",
-    "            plt.xlabel(\"δ_t (hours)\")\n",
-    "            plt.ylabel(\"Event Graph Nodes\")\n",
-    "            plt.title(\"Phase Transition Analysis\")\n",
-    "            plt.legend()\n",
-    "            plt.grid(True)\n",
-    "            plt.xticks(rotation=45)\n",
-    "            plt.ticklabel_format(style='plain')  # Prevent scientific notation\n",
-    "            plt.show()\n",
-    "\n",
-    "    def compute_order_parameters(self, results, temporal_graph):\n",
-    "        \"\"\"\n",
-    "        Compute order parameters (ρE, ρG), susceptibilities (χE, χG), \n",
-    "        and probability distributions P(S_E) and P(S_LT) using the component size dictionaries.\n",
-    "        \"\"\"\n",
-    "\n",
-    "        delta_t_values = np.array([float(dt) for dt in results.keys()])\n",
-    "        \n",
-    "        maxS_E_values = np.array([results[str(dt)][\"S_E\"] for dt in delta_t_values])\n",
-    "        maxS_G_values = np.array([results[str(dt)][\"S_G\"] for dt in delta_t_values])\n",
-    "        maxS_LT_values = np.array([results[str(dt)][\"S_LT\"] for dt in delta_t_values]) \n",
-    "        N_values = np.array([results[str(dt)][\"total_components\"] for dt in delta_t_values])\n",
-    "        N_E = temporal_graph.n  # Total number of nodes in the temporal network\n",
-    "\n",
-    "        all_nodes = np.unique(np.concatenate([self.src_array, self.dst_array]))\n",
-    "        N_G = len(all_nodes)  # Total number of unique nodes\n",
-    "\n",
-    "        # Order Parameters:\n",
-    "        # - ρ_E: Fraction of event-graph nodes in the largest component\n",
-    "        # - ρ_G: Fraction of temporal-network nodes in the largest component\n",
-    "        rho_E = np.where(N_values > 0, maxS_E_values / N_E, 0)  # Prevent divide-by-zero\n",
-    "        rho_G = np.where(N_values > 0, maxS_G_values / N_G, 0)\n",
-    "\n",
-    "        # Initialize susceptibility arrays\n",
-    "        chi_E = np.zeros_like(delta_t_values, dtype=np.float64)\n",
-    "        chi_G = np.zeros_like(delta_t_values, dtype=np.float64)\n",
-    "\n",
-    "        # Initialize distributions for P(S_E) and P(S_LT)\n",
-    "        P_S_E_distributions = {}\n",
-    "        P_S_LT_distributions = {}\n",
-    "\n",
-    "        for i, dt in enumerate(delta_t_values):\n",
-    "\n",
-    "            # --- χE Computation (Event-Based Susceptibility) ---\n",
-    "            component_size_counts = results[str(dt)].get(\"component_sizes\", {})\n",
-    "            if component_size_counts == 0:\n",
-    "                component_size_counts = {}  # Replace invalid value\n",
-    "\n",
-    "            # --- χG Computation (Node-Based Susceptibility) ---\n",
-    "            node_component_sizes = results[str(dt)].get(\"node_component_sizes\", {})\n",
-    "\n",
-    "            # --- S_LT Computation (Lifetime-Based Susceptibility) ---\n",
-    "            component_lifetime_counts = results[str(dt)].get(\"component_lifetimes\", {})\n",
-    "\n",
-    "            if not component_size_counts and not node_component_sizes and not component_lifetime_counts:\n",
-    "                continue  # Skip if no valid components\n",
-    "\n",
-    "            # χE Calculation\n",
-    "            if component_size_counts:\n",
-    "                component_size_counts = {int(k): v for k, v in component_size_counts.items()}\n",
-    "\n",
-    "                component_sizes = np.array(list(component_size_counts.keys()))\n",
-    "                counts = np.array(list(component_size_counts.values()))\n",
-    "\n",
-    "                largest_component_size = component_sizes.max(initial=0)\n",
-    "                if largest_component_size > 0:\n",
-    "                    mask = component_sizes < largest_component_size\n",
-    "                    s_squared_counts = (component_sizes[mask] ** 2) * counts[mask]\n",
-    "                    \n",
-    "                    # Compute susceptibility for χ_E (event-based)\n",
-    "                    chi_E[i] = s_squared_counts.sum() / N_E\n",
-    "\n",
-    "                # Compute probability distribution P(S_E)\n",
-    "                total_S_E_components = sum(component_size_counts.values())\n",
-    "                P_S_E = {size: count / total_S_E_components for size, count in component_size_counts.items()}\n",
-    "                P_S_E_distributions[dt] = P_S_E\n",
-    "\n",
-    "            # χG Calculation\n",
-    "            if node_component_sizes:\n",
-    "                node_component_sizes = {int(k): v for k, v in node_component_sizes.items()}\n",
-    "\n",
-    "                node_sizes = np.array(list(node_component_sizes.keys()))\n",
-    "                node_counts = np.array(list(node_component_sizes.values()))\n",
-    "\n",
-    "                largest_node_component = node_sizes.max(initial=0)\n",
-    "                if largest_node_component > 0:\n",
-    "                    node_mask = node_sizes < largest_node_component\n",
-    "                    s_squared_counts_nodes = (node_sizes[node_mask] ** 2) * node_counts[node_mask]\n",
-    "                    \n",
-    "                    # Compute susceptibility for χ_G (node-based)\n",
-    "                    chi_G[i] = s_squared_counts_nodes.sum() / N_G\n",
-    "\n",
-    "            # Compute probability distribution P(S_LT)\n",
-    "            if component_lifetime_counts:\n",
-    "                total_S_LT_components = sum(component_lifetime_counts.values())\n",
-    "                P_S_LT = {size: count / total_S_LT_components for size, count in component_lifetime_counts.items()}\n",
-    "                P_S_LT_distributions[dt] = P_S_LT\n",
-    "    \n",
-    "        # Replace NaN values with zero\n",
-    "        chi_E = np.nan_to_num(chi_E)\n",
-    "        chi_G = np.nan_to_num(chi_G)\n",
-    "\n",
-    "        return delta_t_values, rho_E, rho_G, chi_E, chi_G, P_S_E_distributions, P_S_LT_distributions\n",
-    "\n",
-    "\n",
-    "    def plot_order_parameters(self, delta_t_values, rho_E, rho_G, chi_E, chi_G):\n",
-    "        \"\"\"\n",
-    "        Plot order parameters (ρE, ρG) and susceptibilities (χE, χG).\n",
-    "        \"\"\"\n",
-    "        plt.figure(figsize=(8,6))\n",
-    "\n",
-    "        plt.plot(delta_t_values, rho_E, 'r-', label=r\"$\\rho_E$\")\n",
-    "        plt.plot(delta_t_values, rho_G, 'r-', alpha=0.5, label=r\"$\\rho_G$\")\n",
-    "\n",
-    "        # Avoid division by zero in normalization\n",
-    "        if np.max(chi_E) > 0:\n",
-    "            plt.plot(delta_t_values, chi_E / np.max(chi_E), 'b-', label=r\"$\\chi_E$\", linestyle='dashed')\n",
-    "\n",
-    "        if np.max(chi_G) > 0:\n",
-    "            plt.plot(delta_t_values, chi_G / np.max(chi_G), 'g-', alpha=0.5, linestyle='dashed', label=r\"$\\chi_G$\")\n",
-    "\n",
-    "        plt.xlabel(r\"$\\delta t$ (time threshold)\")\n",
-    "        plt.ylabel(\"Order Parameter / Susceptibility\")\n",
-    "        plt.title(\"Order Parameters vs. δt\")\n",
-    "        plt.legend()\n",
-    "        plt.grid(True)\n",
-    "        plt.show()\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== DF HEAD ===\n",
-      "Dataset Preview:\n",
-      "   u    i            ts  idx\n",
-      "0  0  277  1.385982e+09    0\n",
-      "1  0  277  1.385982e+09    1\n",
-      "2  0  277  1.385982e+09    2\n",
-      "3  0   21  1.385982e+09    3\n",
-      "4  0  277  1.385982e+09    4\n",
-      "Normalized Timestamp Range: 0.0 - 363560.0\n",
-      "Min Timestamp: 0.0\n",
-      "Max Timestamp: 363560.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "file_path = r\"C:\\Users\\userpc\\Desktop\\first-semester-JMU\\lab-socialnet\\lab-social-network\\highschooltemporalcontacts2013\\edges.csv\"\n",
-    "bipartite = True\n",
-    "\n",
-    "df  = preprocess(file_path)\n",
-    "print(\"=== DF HEAD ===\")\n",
-    "print(\"Dataset Preview:\")\n",
-    "print(df.head())\n",
-    "# df = reindex(df, bipartite=bipartite)\n",
-    "\n",
-    "df['normalized_ts'] = (df['ts'] - df['ts'].min())\n",
-    "\n",
-    "print(\"Normalized Timestamp Range:\", df['normalized_ts'].min(), \"-\", df['normalized_ts'].max())\n",
-    "tedges = list(df[['u', 'i', 'normalized_ts']].itertuples(index=False, name=None))\n",
-    "\n",
-    "temporal_graph = pp.TemporalGraph.from_edge_list(tedges)\n",
-    "total_events = len(tedges)\n",
-    "bipartite_percolation = BipartiteTemporalPercolation(temporal_graph, total_events)\n",
-    "\n",
-    "print(\"Min Timestamp:\", df['normalized_ts'].min())\n",
-    "print(\"Max Timestamp:\", df['normalized_ts'].max())\n",
-    "\n",
-    "delta_t_values = np.linspace(df['normalized_ts'].min(), 12500, 20)\n",
-    "\n",
-    "# analysis_results, reachabilit, percolation_metrics = bipartite_percolation.percolation_analysis(delta_t_values)\n",
-    "\n",
-    "# with open(\"highSchoolDynamic_results/analysis_results.json\", \"w\") as file:\n",
-    "#     json.dump(analysis_results, file, indent=4)\n",
-    "\n",
-    "# Load results from the JSON file\n",
-    "with open(\"highSchoolDynamic_results/analysis_results.json\", \"r\") as file:\n",
-    "    analysis_results = json.load(file)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Correlation between measures:\n",
-      "           S_E       S_G      S_LT\n",
-      "S_E   1.000000  0.821518  0.871081\n",
-      "S_G   0.821518  1.000000  0.991456\n",
-      "S_LT  0.871081  0.991456  1.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "delta_t_values = np.array([float(k) for k in analysis_results.keys()])\n",
-    "\n",
-    "bipartite_percolation.compare_component_measures(analysis_results)\n",
-    "bipartite_percolation.plot_largest_component(analysis_results, delta_t_values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "delta_t_values, rho_E, rho_G, chi_E, chi_G, P_S_E_distributions, P_S_LT_distributions = bipartite_percolation.compute_order_parameters(analysis_results, temporal_graph)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:13: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "<>:21: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "<>:29: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "<>:13: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "<>:21: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "<>:29: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "C:\\Users\\userpc\\AppData\\Local\\Temp\\ipykernel_7960\\3041183610.py:13: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "  plt.scatter(sizes, probabilities, label=f\"$\\delta t$ = {dt/60:.2f} minutes\",\n",
-      "C:\\Users\\userpc\\AppData\\Local\\Temp\\ipykernel_7960\\3041183610.py:21: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "  plt.title(\"Distribution of $P(S_E)$ for Selected $\\delta t$\")\n",
-      "C:\\Users\\userpc\\AppData\\Local\\Temp\\ipykernel_7960\\3041183610.py:29: SyntaxWarning: invalid escape sequence '\\d'\n",
-      "  plt.scatter(sizes, probabilities, label=f\"$\\delta t$ = {dt/60:.2f} minutes\",\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "selected_delta_t_values = [657.8947368421053, 1315.7894736842106, 9868.42105263158]\n",
-    "\n",
-    "markers = {selected_delta_t_values[0]: 'o', selected_delta_t_values[1]: 'd', selected_delta_t_values[2]: 's'} \n",
-    "colors = {selected_delta_t_values[0]: 'tab:blue', selected_delta_t_values[1]: 'tab:orange', selected_delta_t_values[2]: 'tab:red'}\n",
-    "\n",
-    "plt.figure(figsize=(6, 5))\n",
-    "\n",
-    "for dt in selected_delta_t_values:\n",
-    "    if dt in P_S_E_distributions:\n",
-    "        sizes = np.array(list(P_S_E_distributions[dt].keys()))\n",
-    "        probabilities = np.array(list(P_S_E_distributions[dt].values()))\n",
-    "        \n",
-    "        plt.scatter(sizes, probabilities, label=f\"$\\delta t$ = {dt/60:.2f} minutes\", \n",
-    "                    marker=markers[dt], color=colors[dt], alpha=0.8)\n",
-    "\n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlabel(\"$S_E$\")\n",
-    "plt.ylabel(\"$P(S_E)$\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Distribution of $P(S_E)$ for Selected $\\delta t$\")\n",
-    "plt.show()\n",
-    "\n",
-    "for dt in selected_delta_t_values:\n",
-    "    if dt in P_S_LT_distributions:\n",
-    "        sizes = np.array(list(P_S_LT_distributions[dt].keys()))\n",
-    "        probabilities = np.array(list(P_S_LT_distributions[dt].values()))\n",
-    "    \n",
-    "        plt.scatter(sizes, probabilities, label=f\"$\\delta t$ = {dt/60:.2f} minutes\", \n",
-    "                    marker=markers[dt], color=colors[dt], alpha=0.8)\n",
-    "    \n",
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "plt.xlabel(\"$S_{LT}$\")\n",
-    "plt.ylabel(\"$P(S_{LT})$\")\n",
-    "plt.legend(loc=\"upper right\")\n",
-    "plt.title(\"Distribution of $P(S_{LT})$\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Optimized β: 0.1, Optimized ν: 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "analysis_results = {float(k): v for k, v in analysis_results.items()}  \n",
-    "delta_t_values = np.array([float(k) for k in analysis_results.keys()])\n",
-    "critical_delta_t = bipartite_percolation.find_critical_threshold(analysis_results, delta_t_values)\n",
-    "\n",
-    "sizes = [64, 128, 256, 512, 1024, 2048]\n",
-    "\n",
-    "# Optimize β and ν\n",
-    "beta_range = np.linspace(0.1, 0.5, 10) \n",
-    "nu_range = np.linspace(1.0, 2.0, 200)   \n",
-    "\n",
-    "best_beta, best_nu = bipartite_percolation.optimize_scaling_exponents(analysis_results, delta_t_values, sizes, critical_delta_t, beta_range, nu_range)\n",
-    "print(f\"Optimized β: {best_beta}, Optimized ν: {best_nu}\")\n",
-    "\n",
-    "scaled_results = bipartite_percolation.finite_size_scaling(analysis_results, delta_t_values, sizes, critical_delta_t, beta=0.1)\n",
-    "\n",
-    "bipartite_percolation.plot_finite_size_scaling(scaled_results, use_log_scale=False)\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "myDefault",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-- 
GitLab