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Register a new userMaximizing Influence of Leaders in Social Networks
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Added by
Xiaotian Zhou
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The operation of adding edges has been frequently used to the study of opinion dynamics in social networks for various purposes. In this paper, we consider the edge addition problem for the DeGroot model of opinion dynamics in a social network with $n$ nodes and $m$ edges, in the presence of a small number $s ll n$ of competing leaders with binary opposing opinions 0 or 1. Concretely, we pose and investigate the problem of maximizing the equilibrium overall opinion by creating $k$ new edges in a candidate edge set, where each edge is incident to a 1-valued leader and a follower node. We show that the objective function is monotone and submodular. We then propose a simple greedy algorithm with an approximation factor $(1-frac{1}{e})$ that approximately solves the problem in $O(n^3)$ time. Moreover, we provide a fast algorithm with a $(1-frac{1}{e}-epsilon)$ approximation ratio and $tilde{O}(mkepsilon^{-2})$ time complexity for any $epsilon>0$, where $tilde{O}(cdot)$ notation suppresses the ${rm poly} (log n)$ factors. Extensive experiments demonstrate that our second approximate algorithm is efficient and effective, which scales to large networks with more than a million nodes.
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Influence overlap is a universal phenomenon in influence spreading for social networks. In this paper, we argue that the redundant influence generated by influence overlap cause negative effect for maximizing spreading influence. Firstly, we present a theoretical method to calculate the influence overlap and record the redundant influence. Then in term of eliminating redundant influence, we present two algorithms, namely, Degree-Redundant-Influence (DRS) and Degree-Second-Neighborhood (DSN) for multiple spreaders identification. The experiments for four empirical social networks successfully verify the methods, and the spreaders selected by the DSN algorithm show smaller degree and k-core values.
A large amount of content is generated everyday in social media. One of the main goals of content creators is to spread their information to a large audience. There are many factors that affect information spread, such as posting time, location, type of information, number of social connections, etc. In this paper, we look at the problem of finding the best posting time(s) to get high content visibility. The posting time is derived taking other factors into account, such as location, type of information, etc. In this paper, we do our analysis over Facebook pages. We propose six posting schedules that can be used for individual pages or group of pages with similar audience reaction profile. We perform our experiment on a Facebook pages dataset containing 0.3 million posts, 10 million audience reactions. Our best posting schedule can lead to seven times more number of audience reactions compared to the average number of audience reactions that users would get without following any optimized posting schedule. We also present some interesting audience reaction patterns that we obtained through daily, weekly and monthly audience reaction analysis.
While social networks are widely used as a media for information diffusion, attackers can also strategically employ analytical tools, such as influence maximization, to maximize the spread of adversarial content through the networks. We investigate the problem of limiting the diffusion of negative information by blocking nodes and edges in the network. We formulate the interaction between the defender and the attacker as a Stackelberg game where the defender first chooses a set of nodes to block and then the attacker selects a set of seeds to spread negative information from. This yields an extremely complex bi-level optimization problem, particularly since even the standard influence measures are difficult to compute. Our approach is to approximate the attackers problem as the maximum node domination problem. To solve this problem, we first develop a method based on integer programming combined with constraint generation. Next, to improve scalability, we develop an approximate solution method that represents the attackers problem as an integer program, and then combines relaxation with duality to yield an upper bound on the defenders objective that can be computed using mixed integer linear programming. Finally, we propose an even more scalable heuristic method that prunes nodes from the consideration set based on their degree. Extensive experiments demonstrate the efficacy of our approaches.
In previous studies, much attention from multidisciplinary fields has been devoted to understand the mechanism of underlying scholarly networks including bibliographic networks, citation networks and co-citation networks. Particularly focusing on networks constructed by means of either authors affinities or the mutual content. Missing a valuable dimension of network, which is an audience scholarly paper. We aim at this paper to assess the impact that social networks and media can have on scholarly papers. We also examine the process of information flow in such networks. We also mention some observa- tions of attractive incidents that our proposed network model revealed.
Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social networks structure and communicative properties. Here, we study how external events are associated with a networks change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network turtles up. It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.
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