Social network analysis code examples for PyCon 2019 talk

TKDE 2022. CCCL: Contrastive Cascade Graph Learning.

TKDE 2021. CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction.

An official implementation of "Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization"

Replication package for "The Upper Bound of Information Diffusion in Code Review"

A framework for REcommending LInks in SOcial Networks

This is the link for codes and data used in Cascade2vec

ChronoGraph is a temporal graph traversal platform enabling efficient information diffusion analyses over time. The repository reproduces the temporal syntax and the temporal supports presented in the papers in Publication section.

This repository contains FDP'18 presentations and R scripts.

An analysis on the cascading behavior between Taiwanese Instagram food bloggers, based on Asynchronous Independent Cascade Model (AsIC) and Influence Maximization Model.

Information Diffusion Network Analysis

Implementation of the Mineral algorithm as described in the paper, Mineral: Multi-modal Network Representation Learning.

Code and data for the paper: "Message Distortion in Information Cascades" (TheWebConf2019)

The files in this repo were used in the analysis for the paper Branching process descriptions of information cascades on Twitter.

Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction

The code of the paper "Pairwise-interactions-based Bayesian Inference of Network Structure from Cascades"

This study investigates whether considering limited human attention would facilitate information propagation in online social networks.

Code and Model Information for to support the paper: Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, SBP-BRIMS, pp. 114-119.

Agent-Based-Modelling Models

A framework for modeling information diffusion, polarization, synchronization, and echo chamber formation in complex networks.