Holistic Influence Maximization (original) (raw)
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Maximizing the Spread of Positive Influence in Online Social Networks
2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013
Online social networks (OSNs) provide a new platform for product promotion and advertisement. Inäuence maximization problem arising in viral marketing has received a lot of attention recently. Most of the existing diffusion models rely on one fundamental assumption that inäuenced user necessarily adopts the product and encourages his/her friend to further adopt it. However, an inäuenced user may be just aware of the product. Due to personal preference, neutral or negative opinion can be generated so that product adoption is uncertain. Maximizing the total number of inäuenced users is not the uppermost concern, instead, letting more activated users hold positive opinions is of ãrst importance. Motivated by above phenomenon, we proposed model, called Opinion-based Cascading (OC) model. We formulate an opinion maximization problem on the new model to take individual opinion into consideration as well as captures the change of opinions at the same time. We show that under the OC model, opinion maximization is NP-hard and the objective function is no longer submodular, and further prove that there does not exist any approximation algorithm with ãnite ratio unless P=NP. We have designed an efãcient algorithm to compute the total positive inäuence based on this new model. Comprehensive experiments on real social networks are conducted, and results show that previous methods overestimate the overall positive inäuence, while our model is able to distinguish between negative opinions and positive opinions, and estimate the overall inäuence more accurately.
Accelerating influence maximization using heterogeneous algorithms
The Journal of Supercomputing, 2019
Influence maximization (IM) is an interesting study in the domain of social and complex networks which has gained widespread importance in recent years due to the growth in viral marketing and targeted advertisements through the use of online social networks. There have been several enriching contributions which focus on efficient algorithms for IM; a fundamental question remains as to how can the IM computations be done efficiently with better performance that can aid evaluations in real time. In this work, we present a novel mechanism of IM computation, using two case studies HBUTA and HSSA, that utilizes all the available computing hardware present in a current-generation high-end computing system. We present techniques for efficiently partitioning work, computations of IM in a distributed manner, consolidation, and addressing challenges towards ensuring maximum coverage of the input graph. We see that with our initial implementations, we get a maximum of 29.05% and 35.67% improvement in performance due to our HBUTA and HSSA algorithms, respectively, when used on a graph consisting of 1.6 million vertices and 30 million edges.
Efficient Influence Maximization in Social Networks
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching influence spread, (b) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned for a specific influence cascade model, it achieves almost matching influence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes. Based on our results, we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of [5] that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.
Smart Information Spreading for Opinion Maximization in Social Networks
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized learning algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.
Influence Maximization Revisited: The State of the Art and the Gaps that Remain
2019
The steady growth of graph data from social networks has resulted in wide-spread research on the influence maximization (IM) problem. This results in extension of the state-of-the-art almost every year. With the recent explosion in the application of IM in solving real-world problems, it is no longer a theoretical exercise. Today, IM is used in a plethora of real-world scenarios, with OnePlus1 series of mobile phones, Hokey Pokey2 ice-creams, and galleri5 influencer marketplace3 being the most prominent industrial use-cases. Given this scenario, navigating the maze of IM techniques to get an in-depth understanding of their utilities is of prime importance. In this tutorial, we address this paramount issue and solve the dilemma of “Which IM technique to use and under What scenarios”? “What does it really mean to claim to be the state-of-the-art”? This tutorial builds upon our benchmarking study [1], and will provide a concise and intuitive overview of the most important IM techniques...
Maximizing Multifaceted Network Influence
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019
An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is to assign k distinct pieces of an information campaign T to k promoters, so as to achieve the highest viral adoption in a network. We express adoption by users with a logistic model, and show that approximating OIPA within any constant factor is NP-hard. Even so, we propose a branchand-bound framework for OIPA with an (1 − 1/e) approximation ratio. We further optimize this framework with a pruningintensive progressive upper-bound estimation approach, yielding a (1 − 1/e − ε) approximation ratio and significantly lower time complexity, as it relies on the power-law properties of real-world social networks to run efficiently. Our extensive experiments on several real-world datasets show that the proposed approaches consistently outperform intuitive baselines, adopted from stateof-the-art IM algorithms. Furthermore, the progressive approach demonstrates superior efficiency with an up to 24-fold speedup over the plain branch-and-bound approach.
Influence maximization, defined by Kempe, , is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 − 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of Θ( n/k) on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to com- This version is a revision following the SDM camera-ready version. pute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model
2011 IEEE 11th International Conference on Data Mining, 2011
There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Kempe et al.
Influence maximization in social networks when negative opinions may emerge and propagate
… International Conf. on …, 2010
Influence maximization, defined by Kempe, , is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1 − 1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of Θ( n/k) on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to com- This version is a revision following the SDM camera-ready version. pute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.
Influence Maximization in Online Social Networks
Online Social networks are an increasingly important part of our culture. They are now one of the dominant ways in which some people communicate, and the rate of that communication can be almost instantaneous. For that reason, the spread and diffusion of information throughout a network is an interesting phenomenon to understand. It especially can be a useful tool for marketing purposes where, specifically, the influence maximization problem is relevant. The goal of influence maximization is to find any given number of nodes (people) in a network that could spread some specific information to as large a portion of the network as possible. Solutions for this problem have been proposed since about 2003, and already several good approximation algorithms are in use. Current research mostly aims to improve results with novel techniques that focus on estimating more accurate influence probabilities between nodes. Other research in the area aims to include more information such as the susceptibility of certain people to certain information. Still other research aims to find trendsetters of a certain expertise in a network. There are many ways in which we can understand how information spreads in an online social network, and this information can be used as an advantage in influencing an entire online network of people.