Interdependent Networks from Societal Perspective: MITS (Multi-Context Influence Tracking on Social Network)
The real-world system can be represented in terms of multiple complex and semantically coherent networks. The networks have some correlation among each other and complement each other’s functionality. Such correlated networks are termed as interdependent networks. The notion of a smart city can be represented as an integration of several interdependent networks that can facilitate secured and efficient management of a city’s assets, such as transportation, power grids, water supply channel, distributed sensor networks, societal networks, and other services. In this chapter, we introduce the societal perspective of interdependent networks, where the users’ and locations’ networks are exploited to track the influential user and location nodes.
The task of identifying and tracking influential nodes in the ever-growing information networks is crucial to real-world problems that require information propagation (e.g., viral marketing). The exploitation of social networks for influential node detection has been quite popular in the last decade. However, most of the studies have focused on networks with homogeneous nodes (e.g., user-user nodes), and have also ignored the impact of relevant contexts. The information networks have heterogeneous entities that are interconnected and complement each other’s functionality. Hence, the classical techniques popular in modeling the spreading of epidemics in simple networks may not be efficient.
We propose a model called MITS (Multi-context Influence Tracking on Social Network) that represents the contextual exploitation of heterogeneous nodes (i.e., user-location nodes in Location-based Social Networks (LBSN)), formulates the locality-aware spatial-socio-temporal influence tracking problem using Brooks-Iyengar hybrid algorithm, and uses the geo-tagged check-in data to identify and track the locality influence. The empirical evaluation of the proposed model on two real-world datasets, using the Susceptible-Infected-Recovered (SIR) epidemic technique, coverage, and ratio of affection metrics demonstrates a significant performance gain (e.g., 10–85% on coverage and 14–39% on ratio of affection) of the proposed model against other popular techniques, such as degree centrality, betweenness centrality, closeness centrality, and PageRank.
This research is partially supported by US Army Research Lab under the grant number W911NF-12-R-0012 and by FIU Dissertation Year Fellowship.
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