5,281 research outputs found
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 Survey on Location-Driven Influence Maximization
Influence Maximization (IM), which aims to select a set of users from a
social network to maximize the expected number of influenced users, is an
evergreen hot research topic. Its research outcomes significantly impact
real-world applications such as business marketing. The booming location-based
network platforms of the last decade appeal to the researchers embedding the
location information into traditional IM research. In this survey, we provide a
comprehensive review of the existing location-driven IM studies from the
perspective of the following key aspects: (1) a review of the application
scenarios of these works, (2) the diffusion models to evaluate the influence
propagation, and (3) a comprehensive study of the approaches to deal with the
location-driven IM problems together with a particular focus on the
accelerating techniques. In the end, we draw prospects into the research
directions in future IM research
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
Streaming Graph Challenge: Stochastic Block Partition
An important objective for analyzing real-world graphs is to achieve scalable
performance on large, streaming graphs. A challenging and relevant example is
the graph partition problem. As a combinatorial problem, graph partition is
NP-hard, but existing relaxation methods provide reasonable approximate
solutions that can be scaled for large graphs. Competitive benchmarks and
challenges have proven to be an effective means to advance state-of-the-art
performance and foster community collaboration. This paper describes a graph
partition challenge with a baseline partition algorithm of sub-quadratic
complexity. The algorithm employs rigorous Bayesian inferential methods based
on a statistical model that captures characteristics of the real-world graphs.
This strong foundation enables the algorithm to address limitations of
well-known graph partition approaches such as modularity maximization. This
paper describes various aspects of the challenge including: (1) the data sets
and streaming graph generator, (2) the baseline partition algorithm with
pseudocode, (3) an argument for the correctness of parallelizing the Bayesian
inference, (4) different parallel computation strategies such as node-based
parallelism and matrix-based parallelism, (5) evaluation metrics for partition
correctness and computational requirements, (6) preliminary timing of a
Python-based demonstration code and the open source C++ code, and (7)
considerations for partitioning the graph in streaming fashion. Data sets and
source code for the algorithm as well as metrics, with detailed documentation
are available at GraphChallenge.org.Comment: To be published in 2017 IEEE High Performance Extreme Computing
Conference (HPEC
RTIM: a Real-Time Influence Maximization Strategy
International audienceInfluence Maximization (IM) consists in finding in a network the top-k influencers who will maximize the diffusion of information. However, the exponential growth of online advertisement is due to Real-Time Bidding (RTB) which targets users on webpages. It requires complex ad placement decisions in real-time to face a high-speed stream of users. In order to stay relevant, the IM problem should be updated to answer RTB needs. While traditional IM generates a static set of influ-encers, they do not fit with an RTB environment which requires dynamic influence targeting. This paper proposes RTIM, the first IM algorithm capable of targeting users in a RTB environment. We also analyze influence scores of users in several social networks and provide a thorough experimental process to compare static versus dynamic IM solutions
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