发表机构
School of Computer and Information Sciences, University of Hyderabad（海得拉巴大学计算机与信息科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对属性图的链接预测与社区检测难题，提出inc-LPCDAG工作流，实验验证其可有效分析动态属性图的结构与行为过程。
AI 中文摘要
社会系统围绕共同利益的互动和关系维护发挥着至关重要的作用，这些互动和关系是社会的组成部分，可表示为网络，其分析在动态环境中具有关键作用，可通过链接预测和社区检测任务实现。多种链接预测方法可估算新关系，例如社交媒体上的好友推荐；而社会派系是相互连接的人群组成的群体，可揭示社会系统的内部结构，能通过社区检测方法识别。利用这些方法分析社会系统的结构与行为，可理解其演化，为此，将这些方法与基于互动的分析相结合以形成内聚社区，从而实现对社会系统的动态研究。此外，属性信息至关重要，多数现实世界图（如Facebook、Twitter）都具备提供上下文的属性信息，将其与结构信息结合可深入洞察模式，这是一项关键任务。然而，现有方法在处理社会系统动态性方面存在局限，且现有文献中没有方法能为属性图识别潜在链接并形成密集连接的社区。为应对这些挑战，我们提出了一种信息工作流，即inc-LPCDAG（属性图中的增量链接预测与社区检测）。我们开展实验评估效率，结果表明，通过分析和理解结构与行为过程，所提工作流具有有效性。
英文摘要
Social systems play a vital role through interactions around shared interests and the maintenance of relationships, which are part of society. These can be represented as networks, and their analysis plays a crucial role in dynamic environments, which can be achieved through link prediction and community detection tasks. Several link prediction approaches estimate new relationships, such as suggesting friends on social media. On the other hand, a social faction is a group of connected people that reveals the internal structure of the social system and can be identified through community detection approaches. The evolution of social systems can be understood using these approaches by analysing their structure and behaviour. To handle this, a combination of these approaches, along with interaction-based analysis, is used to form cohesive communities, thereby enabling the study of social systems dynamically. In addition, attribute information plays a critical role. Most real-world graphs, like Facebook and Twitter, have attribute information that provides context, and integrating them with structural information offers deeper insights into patterns, which is a critical task. However, existing approaches have limitations in handling dynamics of social systems. Additionally, no existing approaches in the literature help identify potential links and form densely connected communities for attributed graphs. To address these challenges, we have proposed an information workflow, i.e., inc-LPCDAG (\underline{inc}remental Link Prediction and Community Detection in Attributed Graphs). We conducted experiments to evaluate efficiency, and results demonstrate the effectiveness of our proposed workflow by analysing and understanding structural and behavioural processes.
Comments12 pages, 2 figures
