A scikit-learn compatible library for graph kernels

A pytorch adversarial library for attack and defense methods on images and graphs

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

Implementation of the paper "NetGAN: Generating Graphs via Random Walks".

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

Python implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.

My ML related stuff including notebooks, codes and a curated list of various useful resources such as books and softwares. Almost everything mentioned here is free(as speech not free food) or open-source.

A curated list of awesome graph representation learning.

Companion repository for the KDD'18 hands-on tutorial on Higher-Order Data Analytics for Temporal Network Data

Overlapping community detection in Large-Scale Networks using BigCLAM model build on Apache Spark

gBolt--very fast implementation for gSpan algorithm in data mining

Python Implementation for Random Walk with Restart (RWR)

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Core streaming heterogeneous graph clustering and anomaly detection code (KDD 2016)

👪 Social Network Analysis Papers

Mining graph streams using dictionary-based compression

Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".

Graph database developed on Go

Beyond outliers and on to micro-clusters: Vision-guided Anomaly Detection

A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).

Functions for creating and analyzing word co-occurrence networks in Python and R

graph-based substructure pattern mining algorithm (authors: Xifeng Yan, Jiawei Han) implementation

Misc functions for graph plotting and analysis

Learning representations of text and graphs

"Explainable classification of brain networks via contrast subgraphs" - T. Lanciano, F. Bonchi, A. Gionis

Code for " Discovering topically- and temporally-coherent events in interaction networks" ECML PKDD 2016