Set Of Most Influential Nodes Research Articles

CBIM: Community-based influence maximization in multilayer networks

Selecting seed nodes (most influential nodes) in networks has attracted attention due to seed nodes’ ability to influence and spread information. Seed nodes are essential to understanding the spreading and controlling of the information dynamics of the networks. Influence maximization (IM) is predominant in monolayer networks. After the advancements and widespread usage of social networks, applying influence maximization to multilayer networks is gaining popularity. Identifying influential nodes precisely in multilayer networks is a challenging and yet unexplored task. Based on studies, individuals in a community interact frequently and are more likely to influence each other. Motivated by this observation, this paper proposes community-based influence maximization (CBIM) model to find k seed nodes in multilayer networks. CBIM has two phases: In the first phase, CBIM uses the function FIC(M) to find the small communities from a multilayer network based on dice neighborhood similarity. It uses the function CSC(CSinit,θ) to merge smaller communities and generate larger communities to improve communities’ quality. In the second phase, CBIM computes Edge Weight Sum (EWS) for each node in a community and ranks the nodes based on EWS. CBIM uses the quota-based approach to select the seed node set from the communities based on the ranks. A comparative study of various influence maximization (IM) algorithms shows that the CBIM algorithm performs better than the state-of-the-art. The simulation studies have shown that CBIM can detect a set of most influential nodes on real-time datasets under various settings and environments.

Ranking influential spreaders is an ill-defined problem

Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem —methods for identifying influential spreaders output a ranking of the nodes. In this work, we show that such a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Therefore, the set of n most important nodes to vaccinate does not need to have any node in common with the set of n + 1 most important nodes. We propose a method for quantifying the extent and impact of this phenomenon. By this method, we show that it is a common phenomenon in both empirical and model networks.

Analyzing competitive influence maximization problems with partial information: An approximation algorithmic framework

Given the popularity of the viral marketing campaign in online social networks, finding a computationally efficient method to identify a set of most influential nodes so as to compete well with others is of the utmost importance. In this paper, we propose a general model to describe the influence propagation of multiple competing sources in the same network. We formulate the Competitive Influence Maximization with Partial information (CIMP) problem: given an influence propagation model and the probability of a node being in the competitor’s seed set, how to find a set of k seeds so to trigger the largest expected influence cascade under the presence of other competitors? We propose a general algorithmic framework, Two-phase Competitive Influence Maximization (TCIM), to address the CIMP problem. TCIM returns a (1−1/e−ϵ)-approximate solution with probability of at least 1−n−ℓ, where ℓ≥1/2 is a parameter controlling the trade-off between the success probability and the computational efficiency. TCIM has an efficient expected time complexity of O(c(k+ℓ)(m+n)logn/ϵ2), where n and m are the number of nodes and edges in the network, and c is a function of the given propagation model (which may depend on k and the underlying network). To the best of our knowledge, this is the first work which provides a general framework for the competitive influence maximization problem where the seeds of the competitor could be given as an explicit set of seed nodes or a probability distribution of seed nodes. Moreover, our algorithmic framework provides both quality guarantee of solution and practical computational efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, for the case where the seed set of the competitor is given explicitly, we achieve up to four orders of magnitude speedup as compared to previous algorithms with the same quality guarantee. When the competitor’s seed set is not given explicitly, running TCIM using the probability distribution of the competitor’s seeds returns nodes with higher expected influence than those nodes returned by TCIM using an explicit guess of the competitor’s seeds.