Sigcon: Simplifying a Graph Based on Degree Correlation and Clustering Coefficient

摘要: Depicting a complex system like social networks as graph helps understand its structure and relation. As advances in technology increase the amount of data, simplifying large-scale has attracted interests. Simplification reduces size while preserving important properties. In this paper, we propose summarization algorithm to simplify focusing on degree correlation clustering coefficient. The is measure assess influence each vertex their connections. coefficient estimates latent connections between two distinct vertices. To end, first separate into communities. Looking at groups instead itself allows us extract vertices edges more easily. We then categorize communities four cases them different ways preserve innate characteristics. quantitative qualitative evaluations demonstrate how effectively our serves goal. Overall, contributions are follows: (a) unique pattern: found that hubs connected indirectly via low-degree Sigcon preserves these during simplification. (b) efficient algorithm: identifies influential basis connecting effectively.