最大化
杠杆(统计)
不信任
计算机科学
信息级联
启发式
病毒式营销
桥接(联网)
社交网络(社会语言学)
可扩展性
级联
集合(抽象数据类型)
维数之咒
计算
理论计算机科学
数学优化
人工智能
数学
算法
社会化媒体
计算机安全
社会心理学
心理学
化学
色谱法
数据库
万维网
心理治疗师
程序设计语言
作者
Ajitesh Srivastava,Charalampos Chelmis,Viktor K. Prasanna
标识
DOI:10.1145/2808797.2809304
摘要
Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascades scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most "influential" nodes in the network. The majority of prior work however, focuses on unsigned networks where individuals adopt the opinion of their neighbors with certain probability. In real life, relationships between individuals can be positive (e.g., friend of relationship) or negative (e.g. connection between "foes"). According to social theory, people tend to have similar opinions to their friends but opposite of their foes. In this work, we study the problem of competing cascades on signed networks, which has been relatively unexplored. Particularly, we study the progressive propagation of two competing cascades in a signed network under the Independent Cascade Model, and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem. Unlike prior work, we allow the seed-set to be initialized with populations of both cascades with the end goal of maximizing the spread of one cascade. We validate our approach on several large-scale real-world and synthetic networks. Our experiments demonstrate that our influence maximization heuristic significantly outperforms state-of-the-art methods, particularly when the network is dominated by distrust relationships.
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