Lu Yi

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time. By integrating sequence modeling modules into traditional GNN architectures, dynamic GNNs aim to bridge this gap, capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models. We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated. We also discuss large-scale dynamic GNNs and pre-training techniques. Although dynamic GNNs have shown superior performance, challenges remain in scalability, handling heterogeneous information, and lack of diverse graph datasets. The paper also discusses possible future directions, such as adaptive and memory-enhanced models, inductive learning, and theoretical analysis.

We study the fundamental problem of sampling independent events, called subset sampling. Specifically, consider a set of n distinct events S=x1, …, xn, in which each event xi has an associated probability p(xi). The subset sampling problem aims to sample a subset T ⊆ S, such that every xi is independently included in T with probability p(xi). A naive solution is to flip a coin for each event, which takes O(n) time. However, an ideal solution is a data structure that allows drawing a subset sample in time proportional to the expected output size μ=∑i=1n p(xi), which can be significantly smaller than n in many applications. The subset sampling problem serves as an important building block in many tasks and has been the subject of various research for more than a decade.However, the majority of existing subset sampling methods are designed for a static setting, where the events in set S or their associated probabilities remain unchanged over time. These algorithms incur either large query time or update time in a dynamic setting despite the ubiquitous time-evolving events with varying probabilities in real life. Therefore, it is a pressing need, but still, an open problem, to design efficient dynamic subset sampling algorithms.In this paper, we propose ODSS, the first optimal dynamic subset sampling algorithm. The expected query time and update time of ODSS are both optimal, matching the lower bounds of the subset sampling problem. We present a nontrivial theoretical analysis to demonstrate the superiority of ODSS. We also conduct comprehensive experiments to empirically evaluate the performance of ODSS. Moreover, we apply ODSS to a concrete application: Influence Maximization. We empirically show that our ODSS can improve the complexities of existing Influence Maximization algorithms on large real-world evolving social networks.