Public-private graph, where a public network is visible to everyone and every user is also associated with its own small private graph accessed by itself only, widely exists in real-world applications of social networks and financial networks. Most existing work on community search, finding a query-dependent community containing a given query, only studies on a public graph, neglecting the privacy issues in public-private networks. However, considering both the public and private attributes of users enables community search to be more accurate, comprehensive, and personalized to discover hidden patterns. In this paper, we study a novel problem of attributed community search in public-private graphs (ACS-PP), aiming to find a connected k-core community that shares the most keywords with the query node. This problem uncovers structurally cohesive communities, such as interest-based user groups or core teams in collaborative networks. To optimize search efficiency, we propose an integrated scheme of constructing a public global graph index and a private personalized graph index. For the private index, we developed a compact structure of the PP-FP-tree index. The PP-FP-tree is constructed based on the public and private neighbors of the query node in the public-private graph, serving as an efficient index to mine frequent node sets that share the most common attributes with the query node. Extensive experiments on real public-private graph datasets validate both the efficiency and quality of our proposed PP-FP search algorithm against existing competitors. The case study on public-private collaboration networks provides insights into the discovery of public-private communities.

翻译：公私图（public-private graph）中，公共网络对所有人可见，而每个用户还关联其独有的私有子图（仅用户本人可访问），这种结构广泛存在于社交网络和金融网络等实际应用中。现有社区搜索（寻找包含给定查询节点的依赖社区）研究大多仅关注公共图，忽视了公私网络中的隐私问题。然而，同时考虑用户的公共属性和私有属性，能够使社区搜索更精准、全面且个性化，从而发现隐藏模式。本文研究公私图中带属性社区搜索的新问题（ACS-PP），目标在于找到与查询节点共享最多关键词的连通k-核心社区。该问题能够发现结构凝聚的社区，例如基于兴趣的用户群组或协作网络中的核心团队。为优化搜索效率，我们提出了一种综合方案，构建公共全局图索引与私有个性化图索引。针对私有索引，我们开发了紧凑的PP-FP-tree索引结构。该索引基于公私图中查询节点的公共邻居与私有邻居构建，作为高效索引用于挖掘与查询节点共享最多共同属性的频繁节点集。在真实公私图数据集上的大量实验验证了所提出的PP-FP搜索算法相较于现有竞争方法的效率与效果。关于公私协作网络的案例研究为发现公私社区提供了深入见解。