A Malicious Information Traceability Model Based on Neighborhood Similarity and Multiple Types of Interaction
Tun Li, Zhou Li, Kexin Ma, Qian Li, Rong Wang, Chaolong Jia, Yunpeng Xiao · IEEE Transactions on Computational Social Systems · 2024
The open and free nature of online platforms presents challenges for tracing malicious information. To address this, we propose a traceability model based on neighborhood similarity and multitype interaction. First, we propose neighborhood similarity algorithms (D-NTC) to address the universality of malicious information dissemination. This algorithm evaluates the impact of user node importance on malicious information propagation by combining node degrees and the topological overlap of neighboring nodes. Second, we consider the interactive nature of multiple types of elements in the network and construct an interactive module based on user-path-malicious information. This module effectively captures the mutual influence relationships among diverse elements. Additionally, we employ representation learning to optimize the transition probability matrix between elements, leveraging hidden relationships to further characterize their interactive impact. Finally, we propose the NSMTI-Rank algorithm, which tackles the complexity of quantifying the influence of multiple types of elements. Drawing inspiration from mutual reinforcement effects, NSMTI-Rank comprehensively quantifies element influence through an iterative framework. Experimental results demonstrate the effectiveness of our approach in mining user node importance and capturing the interaction information among diverse elements in the network. Moreover, it enables the timely and effective identification of sources of malicious information dissemination.