Version 1.0: Implementation of Algorithm for Time-aware Influence Minimization in Social Networks. For more details about our code, please read our paper: "Xueqin C., Jiajie F., Qing L., Yunjun G., Baihua Z., Lu C., Time-aware Influence Minimization via Blocking Social Networks"

1. This repository contains the full version of our paper.
2. This repository contains the codes and datasets used in our paper.
3. Time-aware Influence Minimization via Blocking Social Networks.

We use six publicly available real-world road networks, including EmailCore, Epinions, Amazon, Youtube, FaceBook and LiveJournal datasets.

All of them can be obtained from [1].

[1] Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data.

The following files are the codes for our proposed algorithms. We implemented all the codes using C++ with CLion 2022.3.2.

1. First we use el2bin.cpp$^{[2]}$ (can be found in genSeed folder) to convert from a graph file in weighted edge list to two binary files that encode the graph and its transpose;

 ./el2bin <input file> <output file> <transpose output file>

2. Then we use fake_seeds.cpp to generate fake_seeds (random or influential). Specifically, when generating influential seeds, we set -m top, and the -f parameter means that the fake seeds will be randomly drawn from the top f-th fraction for generating influential fake seeds, the orders of the nodes are determined by the singleton influence file. When choosing random seeds, just use four parameters (-n -o -k and -m), while setting -m random. The usages are listed as follows (the first line is for influential seeds and the second is for random seeds):

./fake_seeds -n <number of nodes> -o <seed output file>  -k <number of seeds> -m <top> -f <fraction> -s <singleton influence file>

./fake_seeds -n <number of nodes> -o <seed output file>  -k <number of seeds> -m <random> 

3. Then use algorithm (can be found in TESTIM directory) to tackle our problem, algorithm includes:

• TLT: A TM-loss Greedy function for finding the best seeds set for temporal influence minimization;

• Advanced_tlt: The heurist way for solving the problem;

• deadline_solution: For solving variant problem DSTIMIN;

• Minimal_block_solution: For solving variant problem BSTIMIN.

  The usages are listed as follows:

 ./TLT -i <input networkFile> -o <result output file> -fakeseeds <fakeSeed file> -k <budget of blockSet> -epsilon <xx> -t <max edge delay> -T <deadline> -lamda <edge delay parameters> -delta <xx>

 ./Advanced_tlt -i <input networkFile> -o <result output file> -fakeseeds <fakeSeed file> -k <budget of blockSet> -t <max edge delay> -T <deadline> -lamda <edge delay parameters> 

./deadline_solution -i <input networkFile> -o <result output file> -fakeseeds <fakeSeed file> -k <budget of blockSet> -t <max edge delay> -T <deadline> -lamda <edge delay parameters> -preFile <file for determine the max_T and max_alpha> -alpha <the percentage of users influenced>

./Minimal_block_solution -i <input networkFile> -o <result output file> -fakeseeds <fakeSeed file> -t <max edge delay> -T <deadline> -lamda <edge delay parameters> -alpha <the percentage of users influenced>

[2] Michael Simpson, Farnoosh Hashemi, and Laks VS Lakshmanan. 2022. Misinformation mitigation under differential propagation rates and temporal penalties. Proceedings of the VLDB Endowment 15, 10 (2022), 2216–2229.

A 64-bit Linux-based OS.

GCC 4.7.2 and later.