科研之友
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摘要
In many real-world networks, entities of different types usually form an evolving network with hybrid interactions. However, how to theoretically model such networks, along with quantitive characterizations, remains unexplored. Motivated by this, we develop a novel evolving model, which, as validated by our empirical results, can well capture some basic properties such as power-law degree distribution, densification, shrinking diameter, and community structure embodied in most real datasets. Particularly, two types of results are presented in this paper. First, our proposed model, namely, evolving K-Graph, consists of K-node sets representing K different types of entities. The hybrid interactions among entities, based on whether they belong to the same type, are classified into inter-type and intra-type ones that are, respectively, characterized by two joint graphs evolving over time. Following our newly proposed mechanism called interactive-evolution, potential connections can be established among nodes with common features and further form a positive feedback. The superiorities of our model are three folded: good capture of realistic networks, mathematical tractability, and efficient implementation. Second, by analytical derivations, along with empirical validation on real datasets, we disclose two aspects of network properties: basic ones as power-law degree distribution, densification, shrinking diameter and community structure, as well as a distinctive one, that is, positive correlation observed in real networks, implying that a hub in one inter-type relationship network also has many neighbors in another one. An additional interesting finding is that through further comparison of models with or without interactive-evolution, the former one leads to an even earlier occurrence of network connectivity.
- 出版日期2019-2
- 单位上海交通大学
