Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph
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Updated
Oct 8, 2021
Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph
Representation learning on dynamic graphs using self-attention networks
Variational Graph Recurrent Neural Networks - PyTorch
A collection of resources on dynamic/streaming/temporal/evolving graph processing systems, databases, data structures, datasets, and related academic and industrial work
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
CTGCN: k-core based Temporal Graph Convolutional Network for Dynamic Graphs (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9240056
A paper list of research about social knowledge graph
[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."
[NIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"
Code for "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms" (WWW 2021).
[AAAI23] Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
A parallel packed CSR data structure for large-scale dynamic graphs
Visual Analysis of Temporal Summaries in Dynamic Graphs (IEEE TVCG)
DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.
Representation and learning framework for dynamic graphs using Graph Neural Networks.
Source code of "RapidFlow: An Efficient Approach to Continuous Subgraph Matching" published in VLDB'2022 - By Shixuan Sun, Xibo Sun, Bingsheng He and Qiong Luo
Research sources on graph-based anomaly detection
Python 3 supported version for DySAT
DAGGER: Reachability Index for Dynamic Graphs
dynnode2vec is a python package that implements algorithms to embed dynamic graphs
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