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A Community-Aware Approach to Minimizing Dissemination in Graphs

机译:一种社区意识到,可以最大限度地减少图表的传播

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Given a graph, can we minimize the spread of an entity (such as a meme or a virus) while maintaining the graph's community structure (defined as groups of nodes with denser intra-connectivity than inter-connectivity)? At first glance, these two objectives seem at odds with each other. To minimize dissemination, nodes or links are often deleted to reduce the graph's connectivity. These deletions can (and often do) destroy the graph's community structure, which is an important construct in real-world settings (e.g., communities promote trust among their members). We utilize rewiring of links to achieve both objectives. Examples of rewiring in real life are prevalent, such as purchasing products from a new farm since the local farm has signs of mad cow disease; getting information from a new source after a disaster since your usual source is no longer available, etc. Our community-aware approach, called constrCRlink (short for Constraint Community Relink), preserves (on average) 98.6% of the efficacy of the best community-agnostic link-deletion approach (namely, NetMelt~+), but changes the original community structure of the graph by only 4.5%. In contrast, NetMelt~+ changes 13.6% of the original community structure.
机译:给定图形,我们可以最小化实体(例如MEME或病毒)的扩展,同时保持图形的社区结构(定义为具有比连接间的密集帧内的节点组)?乍一看,这两个目标似乎彼此有所不同。为了最小化传播,通常删除节点或链接以降低图形的连接。这些删除可以(并且经常做)破坏图形的社区结构,这是现实世界中的重要构建(例如,社区促进其成员之间的信任)。我们利用链接重新启动以实现两个目标。现实生活中重新疲劳的例子是普遍的,例如自当地农场有疯牛病症状以来的新农场购买产品;自灾难以来从新来源获取信息,因为您的常规来源不再可用等。我们的社区感知方法,称为ContractCrlink(约束社区重新链接的短暂),保留(平均)最佳社区疗效的98.6% -Agnostic Link-删除方法(即NetMelt〜+),但是将图形的原始社区结构更改仅为4.5%。相比之下,Netmelt〜+改变了原始社区结构的13.6%。

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