Recommending Links to Maximize the Influence in Social Networks

Abstract

Social link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim at predicting users' behavior, they accelerate the creation of links that are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances to the majority of users to build new connections and increase their popularity.In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a constant factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.

Reliability Maximization in Uncertain Graphs

“…For multiple-source-target case, we employ Hill Climbing (HC) , Eigenvalue-based Optimization (EO) [16], and two more recent methods, ESSSP [36] and IMA [38], as competitors. Both ESSSP and IMA follow the same manner of adding a budget of new edges into the graph, each with a fixed probability.…”

“…However, such global metric is not query-specific. In real-world, users may tend to optimize the network in a way that is relevant only to themselves, e.g., a campaigner would like to improve the influence [38] of her product to her target customers, but not that of all similar products (from other competitors), and neither to other users who are not her targets. Moreover, many network metrics studied in the past cannot be easily generalized to probabilistic scenarios (e.g., connected component size).…”

Recommending Links to Control Elections via Social Influence

“…Since our Algorithm 1 is a modified version of the algorithm GREEDY2 presented in [25], whose aim was to maximize the influence diffusion in a social network, we can exploit the same reduction to the Limited Seed Selection problem (LSS) that aims at finding a subset of the initial active users in order to maximize the expected number of influenced users. This reduction allows us to adapt the algorithm presented in [26] in such a way that it finds a subset of a given limited set of edges, i.e., S ⊆ (A 0 × V).…”

Adding Edges for Maximizing Weighted Reachability

“…The problem of improving the reachability of a graph is an important graph-theoretical question, which finds applications in several areas. There are several recent possible application scenarios for this problem, for example suggesting friends in a social network in order to increase the spreading of information [1][2][3], reducing the convergence time of random walk processes to perform faster network simulations [4,5], improving wireless sensor networks resilience [6] and even control elections in social networks [7,8].…”