十亿规模下的神经属性社区搜索
Neural Attributed Community Search at Billion Scale
AI总结：
本文提出 ALICE 模型，通过密度草图模块度提取候选子图，并利用一致性感知网络 ConNet 建模查询与图的交互，在大规模图上实现高效、准确且可扩展的属性社区搜索。
摘要：
社区搜索在过去几十年中得到了广泛研究。近年来，属性社区搜索受到越来越多的关注，其目标是根据查询节点和查询属性来识别社区。已有多种技术被研究。尽管近期基于图神经网络（GNN）等先进学习模型的方法在准确率上可以达到最先进的性能，但我们注意到：1）它们存在严重的效率问题；2）它们直接将社区搜索建模为节点分类问题，因此无法充分利用图中不同实体之间的相互依赖关系。受此启发，本文提出了一种面向大规模图的新型神经属性社区搜索模型，称为 ALICE。ALICE 首先提取候选子图以缩小搜索范围，随后通过一致性感知网络 ConNet 预测社区。具体来说，在提取阶段，我们引入了密度草图模块度，它使用统一形式结合了两种现有强大模块度的优势，即经典模块度和密度模块度。基于新的模块度度量，我们首先自适应地获得由查询节点的 k 跳邻居构成的、具有最大模块度的候选子图。然后，我们构建节点-属性二分图以考虑属性信息。之后，ConNet 采用交叉注意力编码器来编码查询与图之间的交互。模型训练由结构-属性一致性和局部一致性引导，以实现更好的性能。在包括一个十亿规模图在内的 11 个真实世界数据集上的大量实验证明了 ALICE 在准确率、效率和可扩展性方面的优越性。
英文摘要：
Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community based on both the query nodes and query attributes. A set of techniques have been investigated. Though the recent methods based on advanced learning models such as graph neural networks (GNNs) can achieve state-of-the-art performance in terms of accuracy, we notice that 1) they suffer from severe efficiency issues; 2) they directly model community search as a node classification problem and thus cannot make good use of interdependence among different entities in the graph. Motivated by these, in this paper, we propose a new neurAL attrIbuted Community sEarch model for large-scale graphs, termed ALICE. ALICE first extracts a candidate subgraph to reduce the search scope and subsequently predicts the community by the Consistency-aware Net , termed ConNet. Specifically, in the extraction phase, we introduce the density sketch modularity that uses a unified form to combine the strengths of two existing powerful modularities, i.e., classical modularity and density modularity. Based on the new modularity metric, we first adaptively obtain the candidate subgraph, formed by the k-hop neighbors of the query nodes, with the maximum modularity. Then, we construct a node-attribute bipartite graph to take attributes into consideration. After that, ConNet adopts a cross-attention encoder to encode the interaction between the query and the graph. The training of the model is guided by the structure-attribute consistency and the local consistency to achieve better performance. Extensive experiments over 11 real-world datasets including one billion-scale graph demonstrate the superiority of ALICE in terms of accuracy, efficiency, and scalability.
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