OPTIMIZING THE SPREAD OF INFLUENCE IN SOCIAL NETWORK COMMUNITY STRUCTURES

Natural disasters and disruptive events were a major reason for researchers to study networks and community vulnerability. Detecting communities is considered a key element to better understand social networks. This detection will allow researchers to discover community structures inside the network and apply several methods to determine influencers inside each community, which in terms will help in evaluating community vulnerability. In this study, Girvan Newman community detection algorithm is applied to detect communities in social networks. This algorithm detects communities based on their betweenness centrality. Several methods have been established to study the spread of influence in social networks such as the Linear Threshold model. Understanding the spread of influence inside communities will help in categorizing community vulnerability. After detecting communities, an influence optimization method using Linear Threshold will be applied to help identifying optimal influencers in each community. The proportion of influencers in each community will be the indicator of social vulnerability. The higher the proportion of influencers in the community, the more resilient the community will be in terms of spreading information inside the network. Sensitivity analysis will be implemented to evaluate the behavior of each community when changes are made to thresholds and the number of initial influencers. The main goal of this study is to identify vulnerable communities and prioritize them, which can help in preparedness for any disruptive event such as natural disasters

Maximizing influence under influence loss constraint in social networks

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, aims to select a small set of users to adopt a product, so that the word-of-mouth effect can subsequently trigger a large cascade of further adoption in social networks. The problem of influence maximization is to select a set of K nodes from a social network so that the spread of influence is maximized over the network. Previous research on mining top-K influential nodes assumes that all of the selected K nodes can propagate the influence as expected. However, some of the selected nodes may not function well in practice, which leads to influence loss of top-K nodes. In this paper, we study an alternative influence maximization problem which is naturally motivated by the reliability constraint of nodes in social networks. We aim to find top-K influential nodes given a threshold of influence loss due to the failure of a subset of R(<K) nodes. To solve the new type of influence maximization problem, we propose an approach based on constrained simulated annealing and further improve its performance through efficiently estimating the influence loss. We provide experimental results over multiple real-world social networks in support. This research will further support practical applications of social networks in various domains particularly where reliability would be a main concern in a system deployment