iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and
  Re-occurrence Preservation
Siwei Zhang Yun Xiong Yao Zhang Xixi Wu Yiheng Sun Jiawei Zhang
Continuous-time dynamic graph modeling is a crucial task for many real-world
applications, such as financial risk management and fraud detection. Though
existing dynamic graph modeling methods have achieved satisfactory results,
they still suffer from three key limitations, hindering their scalability and
further applicability. i) Indiscriminate updating. For incoming edges, existing
methods would indiscriminately deal with them, which may lead to more time
consumption and unexpected noisy information. ii) Ineffective node-wise
long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a
backbone, which has been demonstrated to be incapable of fully capturing
node-wise long-term dependencies in event sequences. iii) Neglect of
re-occurrence patterns. Dynamic graphs involve the repeated occurrence of
neighbors that indicates their importance, which is disappointedly neglected by
existing methods. In this paper, we present iLoRE, a novel dynamic graph
modeling method with instant node-wise Long-term modeling and Re-occurrence
preservation. To overcome the indiscriminate updating issue, we introduce the
Adaptive Short-term Updater module that will automatically discard the useless
or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further
propose the Long-term Updater to realize more effective node-wise long-term
modeling, where we innovatively propose the Identity Attention mechanism to
empower a Transformer-based updater, bypassing the limited effectiveness of
typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are
also encoded into a graph module for informative representation learning, which
will further improve the expressiveness of our method. Our experimental results
on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic
graph modeling.