Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
DynGEM: Deep Embedding Method for Dynamic Graphs
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abstract
Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the embedding for static graphs. However, many graphs in practical applications are dynamic and evolve constantly over time. Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads to unsatisfactory performance in terms of stability, flexibility and efficiency. In this work, we present an efficient algorithm DynGEM based on recent advances in deep autoencoders for graph embeddings, to address this problem. The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph. We test DynGEM on a variety of tasks including graph visualization, graph reconstruction, link prediction and anomaly detection (on both synthetic and real datasets). Experimental results demonstrate the superior stability and scalability of our approach.
verdicts
UNVERDICTED 2representative citing papers
Presents YoutubeGraph-Dyn, a multi-modal dynamic graph dataset from YouTube interactions with intra-day snapshots, and benchmarks clustering for community migration plus time series and RNN methods for forecasting non-timestamped attributes.
citing papers explorer
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
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Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Presents YoutubeGraph-Dyn, a multi-modal dynamic graph dataset from YouTube interactions with intra-day snapshots, and benchmarks clustering for community migration plus time series and RNN methods for forecasting non-timestamped attributes.