Mixed Membership Graph Clustering via Systematic Edge Query
Mixed Membership Graph Clustering via Systematic Edge Query
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DOI：
10.1109/tsp.2021.3109380
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发表时间：
2020-11
影响因子：
5.4
通讯作者：
Shahana Ibrahim;Xiao Fu
中科院分区：
文献类型：
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作者：
Shahana Ibrahim;Xiao Fu
这项工作考虑了在问题设置下的集群节点，只能对边缘进行少量查询，但是整个图形都无法观察到此问题。 ，在限制的调查资源下的社区检测以及隐藏/删除节点互动下的图形拓扑推断。基于编程的低级矩阵完成和基于主动的基于查询的集合发现，许多现有方法旨在估计节点的单群岛成员，但是节点通常会混合（即多群集） 。使用查询边缘的节点的混合成员资格以及系统设计师可以控制和调整的系统查询原理，以适应实施挑战，例如，避免了很难获得我们的框架还采用轻巧且可扩展的算法，并使用成员学习保证。我们方法的有效性。
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is not observable. This problem finds applications in large-scale data clustering using limited annotations, community detection under restricted survey resources, and graph topology inference under hidden/removed node interactions. Prior works tackled this problem from various perspectives, e.g., convex programming-based low-rank matrix completion and active query-based clique finding. Nonetheless, many existing methods are designed for estimating the single-cluster membership of the nodes, but nodes may often have mixed (i.e., multi-cluster) membership in practice. Some query and computational paradigms, e.g., the random query patterns and nuclear norm-based optimization advocated in the convex approaches, may give rise to scalability and implementation challenges. This work aims at learning mixed membership of nodes using queried edges. The proposed method is developed together with a systematic query principle that can be controlled and adjusted by the system designers to accommodate implementation challenges—e.g., to avoid querying edges that are physically hard to acquire. Our framework also features a lightweight and scalable algorithm with membership learning guarantees. Real-data experiments on crowdclustering and community detection are used to showcase the effectiveness of our method.