7 citations · 20 across the 9 of their papers we have counts for
10 papers
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
Zulun Zhu, Siqiang Luo
Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas ma…
Right Answer at the Right Time - Temporal Retrieval-Augmented Generation via Graph Summarization
Zulun Zhu, Haoyu Liu, Mengke He +1
Question answering in temporal knowledge graphs requires retrieval that is both time-consistent and efficient. Existing RAG methods are largely semantic and typically neglect expli…
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Zulun Zhu, Tiancheng Huang, Kai Wang +3
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucinati…
Revisiting Graph Autoencoders as Implicit Contrastive Learners
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs, yet they are often studied in isolatio…
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
Ningyi Liao, Haoyu Liu, Zulun Zhu +2
With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral dom…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
Massively Parallel Single-Source SimRanks in Rounds
Siqiang Luo, Zulun Zhu
SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data management tasks. Th…
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Jintang Li, Zhouxin Yu, Zulun Zhu +6
Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However…
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Yuansheng Wang, Wangbin Sun, Kun Xu +3
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect. To achieve superior performance, the major…
Spiking Graph Convolutional Networks
Zulun Zhu, Jiaying Peng, Jintang Li +3
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemente…