31,488 research outputs found
Opinion-aware Influence Maximization in Online Social Networks
Influence maximization (IM) aims to find seed users on an online social
network to maximize the spread of information about a target product through
word-of-mouth propagation among all users. Prior IM methods mostly focus on
maximizing the overall influence spread, which assumes that all users are
potential customers of the product and that more exposure leads to higher
benefits. However, in real-world scenarios, some users who dislike the product
may express and spread negative opinions, damaging the product's reputation and
lowering its profit. This paper investigates the opinion-aware influence
maximization (OIM) problem, which finds a set of seed users to maximize the
positive opinions toward the product while minimizing the negative opinions. We
propose a novel algorithm for the OIM problem. Specifically, after obtaining
the users with positive and negative opinions towards the product from
historical data, we design a reverse reachable set-based method for
opinion-aware influence estimation and a sandwich approximation algorithm for
seed set selection. Despite the NP-hardness and non-submodularity of OIM, our
algorithm achieves a data-dependent approximation factor for OIM. Experimental
results on three real-world datasets demonstrate that our algorithm improves
the spread of positive opinions while reducing the spread of negative opinions
compared to existing methods.Comment: 7 pages, 4 figures; under revie
Opinion Diversity Maximization in Social Networks
In recent years, the social networks play an important role as the information sources for many people. The algorithms applied by these platforms feed the users with opinions that meet their tastes. These algorithms, on the other hand, harm the free flow of information by forming filter bubbles and echo chambers. The motivation of the thesis is to break filter bubbles by maximizing the diversity of opinion exposures in a social network.
To achieve the goal, we assume that we can change a limited number of users’ exposures to the information in the network. We propose two concrete models, bounded-box diversity maximization and the ℓ2-bounded diversity maximization respectively. Both of them are convex maximization problems subject to different constraints. Besides the common cardinality constraint and linear constraint, the second model has an ℓ2 constraint. We give a detailed exposition of convex maximization problems under different constraints.
We prove that first problem can be discretized and transformed into the Quadratic Knapsack Problem (QKP), while the second one can not. Guided by convexity and QKP, Greedy Algorithms and Semidefinite Relaxation are applied. In a high level, the core idea of the Semidefinite Relaxation consist of two components: first, relaxing the original problem into Semidefinite Programming and obtaining a solution; second, rounding the solution to a feasible solution of the original problem through sampling from a Gaussian distribution.
To evaluate our problem, we implement the algorithms on datasets that have clear chambers and low diversity structure. We show that semidefinite relaxation based algorithms work well when there are large changes on the cardinality, while when the changes are small, the greedy algorithms are comparable. The greedy algorithms have descent scalability for large datasets
Playing Stackelberg Opinion Optimization with Randomized Algorithms for Combinatorial Strategies
From a perspective of designing or engineering for opinion formation games in
social networks, the "opinion maximization (or minimization)" problem has been
studied mainly for designing subset selecting algorithms. We furthermore define
a two-player zero-sum Stackelberg game of competitive opinion optimization by
letting the player under study as the first-mover minimize the sum of expressed
opinions by doing so-called "internal opinion design", knowing that the other
adversarial player as the follower is to maximize the same objective by also
conducting her own internal opinion design.
We propose for the min player to play the "follow-the-perturbed-leader"
algorithm in such Stackelberg game, obtaining losses depending on the other
adversarial player's play. Since our strategy of subset selection is
combinatorial in nature, the probabilities in a distribution over all the
strategies would be too many to be enumerated one by one. Thus, we design a
randomized algorithm to produce a (randomized) pure strategy. We show that the
strategy output by the randomized algorithm for the min player is essentially
an approximate equilibrium strategy against the other adversarial player
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
The steady growth of graph data from social networks has resulted in
wide-spread research in finding solutions to the influence maximization
problem. In this paper, we propose a holistic solution to the influence
maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI)
model that closely mirrors the real-world scenarios. Under the OI model, we
introduce a novel problem of Maximizing the Effective Opinion (MEO) of
influenced users. We prove that the MEO problem is NP-hard and cannot be
approximated within a constant ratio unless P=NP. (2) We propose a heuristic
algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM
heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a
scalable algorithm capable of running within practical compute times on
commodity hardware. In addition to serving as a fundamental building block for
OSIM, EaSyIM is capable of addressing the scalability aspect - memory
consumption and running time, of the IM problem as well.
Empirically, our algorithms are capable of maintaining the deviation in the
spread always within 5% of the best known methods in the literature. In
addition, our experiments show that both OSIM and EaSyIM are effective,
efficient, scalable and significantly enhance the ability to analyze real
datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure
Optimal percolation on multiplex networks
Optimal percolation is the problem of finding the minimal set of nodes such
that if the members of this set are removed from a network, the network is
fragmented into non-extensive disconnected clusters. The solution of the
optimal percolation problem has direct applicability in strategies of
immunization in disease spreading processes, and influence maximization for
certain classes of opinion dynamical models. In this paper, we consider the
problem of optimal percolation on multiplex networks. The multiplex scenario
serves to realistically model various technological, biological, and social
networks. We find that the multilayer nature of these systems, and more
precisely multiplex characteristics such as edge overlap and interlayer
degree-degree correlation, profoundly changes the properties of the set of
nodes identified as the solution of the optimal percolation problem.Comment: 7 pages, 5 figures + appendi
Opinion dynamics with varying susceptibility to persuasion
A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates
- …