This Myket Dataset comprises Android application install interactions from a subset of users in the Myket Android application market.
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Updated
Jun 3, 2024 - Jupyter Notebook
This Myket Dataset comprises Android application install interactions from a subset of users in the Myket Android application market.
TGN-AA: Temporal Graph Networks with attention-based aggregator
[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."
The code for our ICLR 2024 paper: "Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs"
Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"
A collection of resources on dynamic/streaming/temporal/evolving graph processing systems, databases, data structures, datasets, and related academic and industrial work
[ACM Computing Surveys'23] Implementations or refactor of some temporal link prediction/dynamic link prediction methods and summary of related open resources for survey paper "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review" which has been accepted by ACM Computing Surveys.
A* ( Star) algorithm for dynamic graphs on GPU
[TKDE'23] Demo code of the paper entitled "High-Quality Temporal Link Prediction for Weighted Dynamic Graphs via Inductive Embedding Aggregation", which has been accepted by IEEE TKDE
Anomaly Detection in Dynamic Graphs
Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph
DYnamic Attributed Node rolEs (DYANE) is an attributed dynamic-network generative model based on temporal motifs and attributed node behavior.
[AAAI 2023] Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
PyTorch Implementation of a Deep Learning Model for Temporal Link Prediction in MANETs
📈 Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759
CTGCN: k-core based Temporal Graph Convolutional Network for Dynamic Graphs (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9240056
'Explainable' deep learning anomaly detection methods compatible with dynamic graph data
A collection of resources on dynamic/streaming/temporal/evolving graph processing systems, databases, data structures, datasets, and related academic and industrial work
Representation learning on dynamic graphs using self-attention networks
Python 3 supported version for DySAT
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