基于拓扑扩展的在线社交网络恶意信息源定位算法

Malicious Information Source Locating Algorithm Based on Topological Extension in Online Social Network

随着在线社交网络的飞速发展,社交媒体成为网络用户参与的主要平台。恶意信息常常隐藏于在线社交网络的海量数据中,加之拓扑结构的局部性、恶意信息的伪装性,定位和溯源恶意信息面临着很大困难。一方面,仅仅通过人工标注的方式难以实现全局监控,即使借助语义分析、信息搜索等方式也只能在识别热点后获取当前网络中的信息"碎片";另外,信息在演变过程中的变异,使得一条传播链条会中断分裂为多条,不加以识别和区分会增加信息源头数目,大大增加溯源定位的算法复杂度。另一方面,恶意信息常采用伪装手法,如提供虚假的要素、制造热点吸引用户、水军炒作干扰视线等,使得信息拓扑和网络关系拓扑并不一致。原有的定位算法依赖于当前感染节点的分布和当前拓扑,感染状态从单一化向随机化的变化,使得统计推断框架更复杂,需要改进非观测节点的状态推断方法。在线社交网络的信息传播过程中,信息的传播关系常常附加在信息本身中,可以根据当前网络节点的状态挖掘隐藏信息。结合当前网络节点的状态,提出关系拓扑和信息拓扑的概念,并设计基于信息拓扑的候选源点扩展算法。在此基础上,文中提出基于Jordan中心的恶意信息溯源算法。在模型网络和实际网络上的实验表明,相对于对比算法,所提算法能够有效识别出恶意信息源。

Social media on the Internet has become the main interactive platform for online users with the rapid development of online social network.Malicious information often hides in the massive data of online social networks.In addition,the limitation of topologies and the disguise of malicious information bring a lot of difficulties for locating and tra-cing malicious information.On the one hand,it is difficult to achieve global monitoring by means of manual labeling.Even by means of semantic analysis and information search,only the information“fragments”in the current network can be obtained after the hotspots are recognized.Coupled with the variation of information in the evolution process,a chain of propagation will be interrupted and split into multiple pieces.Without identifying and distinguishing,the number of information sources will be increased,and the algorithm complexity will be greatly increased.On the other hand,malicious information often uses camouflage techniques,such as providing false elements,manufacturing hotspots to attract users,and manipulating online“water army”to interfere with the public opinions,which makes the information topology and relation topology inconsistent.The original locating algorithm relies on the distribution of the current infected nodes and the current topology.The singularization of the infection state changes to randomization,making the statistical inference framework more complex.It is necessary to improve the state inference method of non-observed nodes.In the process of information dissemination of online social networks,the information propagation relationship is often attached to the information itself.Therefore,hidden information can be mined according to the state of the current network node.Based on the current state of network nodes,this paper proposed the concepts of relation topology and information topology and designed a candidate source expansion algorithm based on information topology.Based on this,this paper also pre-sented a malicious information locating algorithm based on Jordan center.Experiments on the generated network and real network show that the algorithm can effectively identify malicious information sources compared with other algorithms.