51,709 research outputs found
Continuous Influence-based Community Partition for Social Networks
Community partition is of great importance in social networks because of the
rapid increasing network scale, data and applications. We consider the
community partition problem under LT model in social networks, which is a
combinatorial optimization problem that divides the social network to disjoint
communities. Our goal is to maximize the sum of influence propagation
through maximizing it within each community. As the influence propagation
function of community partition problem is supermodular under LT model, we use
the method of Lov{}sz Extension to relax the target influence
function and transfer our goal to maximize the relaxed function over a matroid
polytope. Next, we propose a continuous greedy algorithm using the properties
of the relaxed function to solve our problem, which needs to be discretized in
concrete implementation. Then, random rounding technique is used to convert the
fractional solution to integer solution. We present a theoretical analysis with
approximation ratio for the proposed algorithms. Extensive experiments
are conducted to evaluate the performance of the proposed continuous greedy
algorithms on real-world online social networks datasets and the results
demonstrate that continuous community partition method can improve influence
spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043
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
A Multi-phase Approach for Improving Information Diffusion in Social Networks
For maximizing influence spread in a social network, given a certain budget
on the number of seed nodes, we investigate the effects of selecting and
activating the seed nodes in multiple phases. In particular, we formulate an
appropriate objective function for two-phase influence maximization under the
independent cascade model, investigate its properties, and propose algorithms
for determining the seed nodes in the two phases. We also study the problem of
determining an optimal budget-split and delay between the two phases.Comment: To appear in Proceedings of The 14th International Conference on
Autonomous Agents & Multiagent Systems (AAMAS), 201
Maximizing Activity in Ising Networks via the TAP Approximation
A wide array of complex biological, social, and physical systems have
recently been shown to be quantitatively described by Ising models, which lie
at the intersection of statistical physics and machine learning. Here, we study
the fundamental question of how to optimize the state of a networked Ising
system given a budget of external influence. In the continuous setting where
one can tune the influence applied to each node, we propose a series of
approximate gradient ascent algorithms based on the Plefka expansion, which
generalizes the na\"{i}ve mean field and TAP approximations. In the discrete
setting where one chooses a small set of influential nodes, the problem is
equivalent to the famous influence maximization problem in social networks with
an additional stochastic noise term. In this case, we provide sufficient
conditions for when the objective is submodular, allowing a greedy algorithm to
achieve an approximation ratio of . Additionally, we compare the
Ising-based algorithms with traditional influence maximization algorithms,
demonstrating the practical importance of accurately modeling stochastic
fluctuations in the system
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Influence Maximization is an extensively-studied problem that targets at
selecting a set of initial seed nodes in the Online Social Networks (OSNs) to
spread the influence as widely as possible. However, it remains an open
challenge to design fast and accurate algorithms to find solutions in
large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not
scalable, while other heuristic algorithms do not have any theoretical
guarantee and they have been shown to produce poor solutions for quite some
cases. In this paper, we propose hop-based algorithms that can easily scale to
millions of nodes and billions of edges. Unlike previous heuristics, our
proposed hop-based approaches can provide certain theoretical guarantees.
Experimental evaluations with real OSN datasets demonstrate the efficiency and
effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page
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