8,914 research outputs found
Adaptive Submodular Influence Maximization with Myopic Feedback
This paper examines the problem of adaptive influence maximization in social
networks. As adaptive decision making is a time-critical task, a realistic
feedback model has been considered, called myopic. In this direction, we
propose the myopic adaptive greedy policy that is guaranteed to provide a (1 -
1/e)-approximation of the optimal policy under a variant of the independent
cascade diffusion model. This strategy maximizes an alternative utility
function that has been proven to be adaptive monotone and adaptive submodular.
The proposed utility function considers the cumulative number of active nodes
through the time, instead of the total number of the active nodes at the end of
the diffusion. Our empirical analysis on real-world social networks reveals the
benefits of the proposed myopic strategy, validating our theoretical results.Comment: Accepted by IEEE/ACM International Conference Advances in Social
Networks Analysis and Mining (ASONAM), 201
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Influence Maximization in Social Networks: A Survey
Online social networks have become an important platform for people to
communicate, share knowledge and disseminate information. Given the widespread
usage of social media, individuals' ideas, preferences and behavior are often
influenced by their peers or friends in the social networks that they
participate in. Since the last decade, influence maximization (IM) problem has
been extensively adopted to model the diffusion of innovations and ideas. The
purpose of IM is to select a set of k seed nodes who can influence the most
individuals in the network.
In this survey, we present a systematical study over the researches and
future directions with respect to IM problem. We review the information
diffusion models and analyze a variety of algorithms for the classic IM
algorithms. We propose a taxonomy for potential readers to understand the key
techniques and challenges. We also organize the milestone works in time order
such that the readers of this survey can experience the research roadmap in
this field. Moreover, we also categorize other application-oriented IM studies
and correspondingly study each of them. What's more, we list a series of open
questions as the future directions for IM-related researches, where a potential
reader of this survey can easily observe what should be done next in this
field
Fairness in Influence Maximization through Randomization
The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in the area of information spread in networks. Similar to previous works like Fish et al. (WWW '19) and Tsang et al. (IJCAI '19), we study the maximin criterion for (group) fairness. By allowing randomized solutions, we introduce two different variants of this problem. While the original deterministic maximin problem has been shown to be inapproximable, interestingly, we show that both probabilistic variants permit approximation algorithms with a constant multiplicative factor of 1 - 1/e plus an additive arbitrarily small error due to the simulation of the information spread. For an experimental study, we provide implementations of our methods and compare the achieved fairness values to existing methods. Non-surprisingly, the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This confirms that studying fairness via randomization is a worthwhile direction. More surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies, computed by our routines dominate over the fairness achieved by previous methods on most of the instances tested
Giving or Taking: The Role of Dispositional Power Motivation and Positive Affect in Profit Maximization?
Socio-economic decisions are commonly explained by rational cost vs. benefit considerations, whereas person variables have not usually been considered. The present study aims at investigating the degree to which dispositional power motivation and affective states predict socio-economic decisions. The power motive was assessed both indirectly and directly using a TAT-like picture test and a power motive self-report, respectively. After nine months, 62 students completed an affect rating and performed on a money allocation task (Social Values Questionnaire). We hypothesized and confirmed that dispositional power should be associated with a tendency to maximize oneâs profit but to care less about another partyâs profit. Additionally, positive affect showed effects in the same direction. The results are discussed with respect to a motivational approach explaining socio-economic behaviour.economic decision-making, rational choice theory, personality, implicit power motive, positive affect, operant motive test
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