富属性网络中的顶点提名
Vertex Nomination in Richly Attributed Networks
AI总结：
本研究针对富属性网络顶点提名任务，从理论与实践层面探讨内容属性和网络拓扑的双重作用，给出二者结合方案优于单一方案的充要条件，为理解网络特征与拓扑的互补性提供新理论基础。
摘要：
顶点提名是一种弱监督的网络信息检索任务，该任务利用一个图中的感兴趣顶点查询第二个图，以发现第二个图中的感兴趣顶点。与其他信息检索任务类似，顶点提名方案的输出是第二个图中顶点的排序列表，迄今未知的感兴趣顶点理想情况下会集中在列表顶部。顶点提名方案为高效挖掘复杂网络中的相关信息提供了一套有用的工具。在本文中，我们从理论和实践两方面探讨了内容（即边和顶点属性）与上下文（即网络拓扑）在顶点提名中的双重作用。我们给出了同时利用内容和上下文的顶点提名方案优于仅单独利用内容或上下文的方案的充要条件。尽管内容与上下文的联合效用已在文献中得到实证证明，但本文提出的框架为理解网络特征与拓扑的潜在互补作用提供了全新的理论基础。
英文摘要：
Vertex nomination is a lightly-supervised network information retrieval task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other information retrieval tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both theoretically and practically, the dual roles of content (i.e., edge and vertex attributes) and context (i.e., network topology) in vertex nomination. We provide necessary and sufficient conditions under which vertex nomination schemes that leverage both content and context outperform schemes that leverage only content or context separately. While the joint utility of both content and context has been demonstrated empirically in the literature, the framework presented in this paper provides a novel theoretical basis for understanding the potential complementary roles of network features and topology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
