Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repeatedly computing such features can be time-consuming due to the high-order subgraph construction. We develop the Co-Neigh...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
30.07.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Structure encoding has proven to be the key feature to distinguishing links
in a graph. However, Structure encoding in the temporal graph keeps changing as
the graph evolves, repeatedly computing such features can be time-consuming due
to the high-order subgraph construction. We develop the Co-Neighbor Encoding
Schema (CNES) to address this issue. Instead of recomputing the feature by the
link, CNES stores information in the memory to avoid redundant calculations.
Besides, unlike the existing memory-based dynamic graph learning method that
stores node hidden states, we introduce a hashtable-based memory to compress
the adjacency matrix for efficient structure feature construction and updating
with vector computation in parallel. Furthermore, CNES introduces a
Temporal-Diverse Memory to generate long-term and short-term structure encoding
for neighbors with different structural information. A dynamic graph learning
framework, Co-Neighbor Encoding Network (CNE-N), is proposed using the
aforementioned techniques. Extensive experiments on thirteen public datasets
verify the effectiveness and efficiency of the proposed method. |
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DOI: | 10.48550/arxiv.2407.20871 |