The paper discusses a method for detecting subgraphs composed of unilateral preferences in directed temporal social networks. The key points are:
Existing research has observed one-sided communication structures where an adult-assumed user sends messages primarily to a minor-assumed user. Automatically detecting such unilateral preference subgraphs could enable the identification of communication motivated by specific intentions, such as online luring.
The authors construct a bottom-up method to detect these unilateral preference subgraphs within complex network structures. They hypothesize that some of these subgraphs may involve dangerous communication like luring, and observing them could provide insights for the safe use of online social networks.
The paper reviews previous research on network dynamics, statistical analysis of networks, and percolation theory as relevant background. It then outlines the proposed method for generating a network with clusters exhibiting different communication behaviors (unilateral sending, unilateral receiving, information dissemination, information blocking, and alert sending).
The authors simulate the temporal dynamics of this network and analyze the degree distributions, asymmetry indices, and mutual friendship densities of the clusters over time. They discuss the potential implications for opinion formation, information flow, and the identification of risky communication patterns in online social networks.
The paper concludes by identifying the largest weakly connected component of the network and performing k-core decomposition to visualize the structural properties. The characteristics of the inferred clusters are discussed in the context of real-world social media ecosystems and opinion formation processes.
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arxiv.org
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