Maximizing the spread of influence through a social network

Abstract

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform nodeselection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

Vulnerability of clustering under node failure in complex networks

“…Ertem et al (2016) studied the problem of how to detect groups of nodes in a social network with high clustering coefficient; however, their work does not consider the vulnerability of the average clustering coefficient of a network. The diffusion of information in a social network has been studied from many perspectives, including worm containment (Nguyen et al, 2010), viral marketing (Dinh et al, 2012aKempe et al, 2003;Kuhnle et al, 2017), and the detection of overlapping communities (Nguyen et al, 2011).…”

“…To observe the effect of ALCC on influence propagation, we adopted the following two standard models (Kempe et al, 2003); intuitively, the idea of a model of influence propagation in a network is a way by which nodes can be activated given a set of seed nodes. An instance of influence propagation on a graph G follows the independent cascade (IC) model if a weight can be assigned to each edge such that the propagation probabilities can be computed as follows: once a node u first becomes active, it is given a single chance to activate each currently inactive neighbor v with probability proportional to the weight of the edge (u, v).…”

From Big to Little Data for Natural Disaster Recovery: How Online and On-the-Ground Activities are Connected?

“…Several studies have employed network analysis for identifying influential members. Several propose models or algorithms for the identification of nodes that have maximum influence in spreading information through a social network (See Chen, Wang, and Yang, 2009;Kempe, Kleinberg, and Tardos, 2003). Studies have used different indexes and methods for analyzing the influence of nodes in networks.…”

A system for extracting structural information from Social Network accounts

“…Unfortunately, in real cases, the difficult task of efficiently accessing and fruitfully querying such a complex and huge source of information must be addressed. In fact, despite the presence of a number of algorithmic solutions and theoretical approaches , no practical/effective tool exists to support applications and research activities needing a massive utilization of cross‐social‐network data (different from what happens in other application contexts managing large amounts of data ). This strongly limits the possibility of developing internetworking applications and research activities based on the information power coming from social data.…”