[论文解读] A Self-Learning Information Diffusion Model for Smart Social Networks

Qi Xuan, Xincheng Shu+4 more|arXiv (Cornell University)|Nov 11, 2018

Complex Network Analysis Techniques参考文献 20被引用 3

一句话总结

本文提出了一种在智能社交网络中的自学习信息传播模型，其中个体根据信息的真实性来更新信任度，使网络能够演化为更准确地区分真实与虚假信息。该模型表明，自学习可增强社会分层，并在互联链式网络中产生‘交叉优势’，提升桥接节点的影响力。

ABSTRACT

In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia. It is widely believed that social networks play important role in the process of information diffusion. However, the opposite question, i.e., how does information diffusion process rebuild social networks, has been largely ignored. In this paper, we propose a new framework for understanding this reversing effect. Specifically, we first introduce a novel information diffusion model on social networks, by considering two types of individuals, i.e., smart and normal individuals, and two kinds of messages, true and false messages. Since social networks consist of human individuals, who have self-learning ability, in such a way that the trust of an individual to one of its neighbors increases (or decreases) if this individual received a true (or false) message from that neighbor. Based on such a simple self-learning mechanism, we prove that a social network can indeed become smarter, in terms of better distinguishing the true message from the false one. Moreover, we observe the emergence of social stratification based on the new model, i.e., the true messages initially posted by an individual closer to the smart one can be forwarded by more others, which is enhanced by the self-learning mechanism. We also find the crossover advantage, i.e., interconnection between two chain networks can make the related individuals possessing higher social influences, i.e., their messages can be forwarded by relatively more others. We obtained these results theoretically and validated them by simulations, which help better understand the reciprocity between social networks and information diffusion.

研究动机与目标

• 研究信息传播如何通过个体的自学习机制重塑社交网络。
• 基于信息的真实性（真实/虚假）建立社交网络中信任动态演化的模型。
• 分析网络结构中社会分层与影响力差异的出现机制。
• 量化自学习导致的互联链式网络中‘交叉优势’的程度。
• 通过模拟与理论分析验证理论发现。

提出的方法

• 提出一种新颖的信息传播模型，包含两类节点：智能个体与普通个体，以及两类信息：真实信息与虚假信息。
• 采用自学习机制：若收到真实信息，则对邻居的信任度上升；若收到虚假信息，则信任度下降。
• 定义信息过滤能力（IFA）以衡量网络智能，并追踪其随时间的改进。
• 使用k-shell分解与网络基序分析（链式与星型网络）研究结构对传播的影响。
• 应用理论分析与模拟验证社会分层与交叉优势的出现。
• 采用迭代训练，通过参数δ与Δ模拟信息传播中长期学习效应。

实验结果

研究问题

• RQ1 个体的自学习如何影响网络区分真实与虚假信息的能力？
• RQ2 在链式网络中，引入智能节点对信息传播产生何种结构与动态影响？
• RQ3 将两个链式网络互联后，如何影响桥接节点的社会影响力？
• RQ4 自学习机制在多大程度上增强了社会分层与交叉优势？
• RQ5 理论预测的信息传播是否可通过模拟得到验证？

主要发现

• 自学习机制显著提升了网络的信息过滤能力（IFA），使网络能更智能地区分真实与虚假信息。
• 社会分层自然形成：距离智能节点越近的节点，由于信任度提高，其信息被更广泛转发。
• 交叉优势得到定量验证：在互联链式网络中，桥接节点在训练后获得更高的社会影响力，转发更多信息。
• 自学习机制同时增强了社会分层与交叉优势，尤其体现在链式网络中从v4到vN的节点上。
• 模拟结果与理论预测高度一致，D_T(i)与D_F(i)的分析值与模拟值表现出强吻合。
• 在特定初始信任条件（η=0.3, 0.5）下，训练后节点v2与v3的社会分层关系发生反转，表明影响力发生动态变化。