Influence Maximization In Social Networks Research Articles

Location‐aware targeted influence maximization in social networks

In this paper, we study the location‐aware targeted influence maximization problem in social networks, which finds a seed set to maximize the influence spread over the targeted users. In particular, we consider those users who have both topic and geographical preferences on promotion products as targeted users. To efficiently solve this problem, one challenge is how to find the targeted users and compute their preferences efficiently for given requests. To address this challenge, we devise a TR‐tree index structure, where each tree node stores users' topic and geographical preferences. By traversing the TR‐tree in depth‐first order, we can efficiently find the targeted users. Another challenge of the problem is to devise algorithms for efficient seeds selection. We solve this challenge from two complementary directions. In one direction, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose two efficient approximation algorithms with approximation ratio, which prune some candidate seeds with small influences by precomputing users' initial influences offline and estimating the upper bound of their marginal influences online. In the other direction, we propose a fast heuristic algorithm to improve efficiency. Experiments conducted on real‐world data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

Community-based influence maximization in social networks under a competitive linear threshold model

The main purpose in influence maximization, which is motivated by the idea of viral marketing in social networks, is to find a subset of key users that maximize influence spread under a certain propagation model. A number of studies have been done over the past few years that try to solve this problem by considering a non-adversarial environment in which there exists only one player with no competitor. However, in real world scenarios, there is always more than one player competing with other players to influence the most nodes. This is called competitive influence maximization. Motivated by this, we try to solve the competitive influence maximization problem by proposing a new propagation model which is an extension of the Linear Threshold model and gives decision-making ability to nodes about incoming influence spread. We also propose an efficient algorithm for finding the influential nodes in a given social graph under the proposed propagation model which exploits the community structure of this graph to compute the spread of each node locally within its own community. The aim of our algorithm is to find the minimum number of seed nodes which can achieve higher spread in comparison with the spread achieved by nodes selected by other competitor. Our experiments on real world and synthetic datasets show that our approach can find influential nodes in an acceptable running time.

Seed activation scheduling for influence maximization in social networks

This paper addresses the challenge of strategically maximizing the influence spread in a social network, by exploiting cascade propagators termed “seeds”. It introduces the Seed Activation Scheduling Problem (SASP) that chooses the timing of seed activation under a given budget, over a given time horizon, in the presence/absence of competition. The SASP is framed as a blogger-centric marketing problem on a two-level network, where the decisions are made to buy sponsored posts from prominent bloggers at calculated points in time. A Bayesian evidence diffusion model – the Partial Parallel Cascade (PPC) model – allows the network nodes to be partially activated, proportional to their accumulated evidence levels. The SASP under the PPC model is proven NP-hard. A mixed-integer program is presented for the SASP, along with an efficient column generation heuristic. The paper sets up its problem instances in real-world settings, taking web-based marketing as an application example. Favorable optimality gaps are achieved for SASP solutions on networks based on observed user interactions in pro-health discussion forums. The presented analyses highlight a trade-off between early and late seed activation in igniting and maintaining influence cascades over time. The results reveal the importance of early seeds for campaigns that favor longevity, e.g., in service industry, and the importance of late seeds for campaigns with deadline(s), e.g., in political competitions.

Continuous states latency aware influence maximization in social networks

Continuous states latency aware influence maximization in social networks

Two-Phase Influence Maximization in Social Networks with Seed Nodes and Referral Incentives

The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most solution approaches available in the existing literature devote the entire budget towards triggering diffusion at seed nodes. This paper investigates the effect of splitting the budget across two different, sequential phases. In phase 1, we adopt the classical approach of initiating diffusion at a selected seed-set. In phase 2, we use the remaining budget to offer referral incentives. We formulate this problem and explore suitable ways to split the budget between the two phases, with detailed experiments on synthetic and real-world datasets. The principal findings from our study are: (a) when the budget is low, it is prudent to use the entire budget for phase 1; (b) when the budget is moderate to high, it is preferable to use much of the budget for phase 1, while allocating the remaining budget to phase 2; (c) in the presence of moderate to strict temporal constraints, phase 2 is not warranted; (d) if the temporal constraints are low or absent, phase 2 yields a decisive improvement in influence spread.

A heat diffusion model based algorithm for personalized influence maximization in social network

A heat diffusion model based algorithm for personalized influence maximization in social network

Identifying multiple influential spreaders by a heuristic clustering algorithm

The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant.

Big social network influence maximization via recursively estimating influence spread

Influence maximization aims to find a set of highly influential nodes in a social network to maximize the spread of influence. Although the problem has been widely studied, it is still challenging to design algorithms to meet three requirements simultaneously, i.e., fast computation, guaranteed accuracy, and low memory consumption that scales well to a big network. Existing heuristic algorithms are scalable but suffer from unguaranteed accuracy. Greedy algorithms such as CELF [1] are accurate with theoretical guarantee but incur heavy simulation cost in calculating the influence spread. Moreover, static greedy algorithms are accurate and sufficiently fast, but they suffer extensive memory cost. In this paper, we present a new algorithm to enable greedy algorithms to perform well in big social network influence maximization. Our algorithm recursively estimates the influence spread using reachable probabilities from node to node. We provide three strategies that integrate memory cost and computing efficiency. Experiments demonstrate the high accuracy of our influence estimation. The proposed algorithm is more than 500 times faster than the CELF algorithm on four real world data sets.

A new heuristic for influence maximization in social networks

A new heuristic for influence maximization in social networks

A New Method for Influence Maximization in Social Networks

A New Method for Influence Maximization in Social Networks

An Efficient Memetic Algorithm for Influence Maximization in Social Networks

Influence maximization is to extract a small set of nodes from a social network which influences the propagation maximally under a cascade model. In this paper, we propose a memetic algorithm for community-based influence maximization in social networks. The proposed memetic algorithm optimizes the 2-hop influence spread to find the most influential nodes. Problem-specific population initialization and similarity-based local search are designed to accelerate the convergence of the algorithm. Experiments on three realworld datasets demonstrate that our algorithm has competitive performances to the comparing algorithms in terms of effectiveness and efficiency. For example, on a real-world network of 15233 nodes and 58891 edges, the influence spread of the proposed algorithm is 12.5%, 13.2% and 173.5% higher than the three comparing algorithms Degree, PageRank and Random, respectively.

Influence maximization in social networks based on discrete particle swarm optimization

Influence maximization in social networks aims to find a small group of individuals, which have maximal influence cascades. In this study, an optimization model based on a local influence criterion is established for the influence maximization problem. The local influence criterion can provide a reliable estimation for the influence propagations in independent and weighted cascade models. A discrete particle swarm optimization algorithm is then proposed to optimize the local influence criterion. The representations and update rules for the particles are redefined in the proposed algorithm. Moreover, a degree based heuristic initialization strategy and a network-specific local search strategy are introduced to speed up the convergence. Experimental results on four real-world social networks demonstrate the effectiveness and efficiency of the proposed algorithm for influence maximization.

Solution of Bharathi–Kempe–Salek conjecture for influence maximization on arborescence

Influence maximization is an important problem in social networks. Bharathi et al. (Competitive influence maximization in social networks, pp 306---311, 2007) conjectured that this problem is $${{\mathcal {N}}}{{\mathcal {P}}}$$NP-hard on arborescence directed into a root. In this short note, we show that the conjecture is true for the independent cascade (IC) model, which is the most studied model in the literature specifying how each node influences other nodes. Therefore, assuming $${\mathcal {P}}\ne {{\mathcal {N}}}{{\mathcal {P}}}$$PźNP, there exists no polynomial-time algorithm for the influence maximization problem under the IC model on arborescence directed into a root. On the other hand, Wang et al. (J Comb Optim, doi:10.1007/s10878-016-9991-1, 2016) have shown that there exists polynomial-time algorithm for this problem under the linear threshold (LT) model. Hence, this pair of results is of theoretical interest since this is the first time to give a theoretical difference on computational complexity between the IC and LT models. We believe it may motivate further research for influence maximization on arborescence and other special graphs.

Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?

Trends in online social media always reflect the collective attention of a vast number of individuals across the network. For example, Internet slang words can be ubiquitous because of social memes and online contagions in an extremely short period. From Weibo, a Twitter-like service in China, we find that the adoption of popular Internet slang words experiences two peaks in its temporal evolution, in which the former is relatively much lower than the latter. This interesting phenomenon in fact provides a decent window to disclose essential factors that drive the massive diffusion underlying trends in online social media. Specifically, the in-depth comparison between diffusions represented by different peaks suggests that more attention from the crowd at early stage of the propagation produces large-scale coverage, while the dominant participation of opinion leaders at the early stage just leads to popularity of small scope. Our results quantificationally challenge the conventional hypothesis of influentials. And the implications of these novel findings for marketing practice and influence maximization in social networks are also discussed.

Time-sensitive influence maximization in social networks

One of the fundamental issues in social networks is the influence maximization problem, where the goal is to identify a small subset of individuals such that they can trigger the largest number of members in the network. In real-world social networks, the propagation of information from a node to another may incur a certain amount of time delay; moreover, the value of information may decrease over time. So not only the coverage size, but also the propagation speed matters. In this paper, we propose the Time-Sensitive Influence Maximization (TSIM) problem, which takes into account the time dependence of the information value. Considering the time delay aspect, we develop two diffusion models, namely the Delayed Independent Cascade model and the Delayed Linear Threshold model. We show that the TSIM problem is NP-hard under these models but the spread function is monotone and submodular. Thus, a greedy approximation algorithm can achieve a 1 − 1/ e approximation ratio. Moreover, we propose two time-sensitive centrality measures and compare their performance with the greedy algorithm. We evaluate our methods on four real-world datasets. Experimental results show that the proposed algorithms outperform existing methods, which ignore the decay of information value over time.

A Review on Models and Algorithms to Achieve Influence Maximization in Social Networks

A major research problem in online social network is Influential node identification which has large number of ties in the network. Influence maximization finds an influential node and maximizes information diffusion. Organizations started to use information diffusion feature in marketing to improve their product promotion. To achieve influence maximization approximation algorithms and diffusion models are widely used. The existing methods consider degree and structure of the network in influential node identification. Most of the existing works consider number of nearest neighbors, user’s connectivity based on user’s rating. Influential nodes are mainly used in marketing and also used in various applications such as public opinion, healthcare, communication, education, agriculture, and epidemiology.

Influence maximization in social networks under an independent cascade-based model

The rapid growth of online social networks is important for viral marketing. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. An independent cascade-based model for influence maximization, called IMIC-OC, was proposed to calculate positive influence. We assumed that influential users spread positive opinions. At the beginning, users held positive or negative opinions as their initial opinions. When more users became involved in the discussions, users balanced their own opinions and those of their neighbors. The number of users who did not change positive opinions was used to determine positive influence. Corresponding influential users who had maximum positive influence were then obtained. Experiments were conducted on three real networks, namely, Facebook, HEP-PH and Epinions, to calculate maximum positive influence based on the IMIC-OC model and two other baseline methods. The proposed model resulted in larger positive influence, thus indicating better performance compared with the baseline methods.

On the Upper Bounds of Spread for Greedy Algorithms in Social Network Influence Maximization

Influence maximization, defined as finding a small subset of nodes that maximizes spread of influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear Threshold (LT) models, where many greedy-based algorithms have been proposed with the best approximation guarantee. However, existing greedy-based algorithms are inefficient on large networks, as it demands heavy Monte-Carlo simulations of the spread functions for each node at the initial step [7] . In this paper, we establish new upper bounds to significantly reduce the number of Monte-Carlo simulations in greedy-based algorithms, especially at the initial step. We theoretically prove that the bound is tight and convergent when the summation of weights towards (or from) each node is less than 1. Based on the bound, we propose a new Upper Bound based Lazy Forward algorithm ( UBLF in short) for discovering the top-k influential nodes in social networks. We test and compare UBLF with prior greedy algorithms, especially CELF [30] . Experimental results show that UBLF reduces more than 95 percent Monte-Carlo simulations of CELF and achieves about $2\hbox{-}10$ times speedup when the seed set is small.

Intensional data on the web

We call data intensional when it is not directly available, but must be accessed through a costly interface. Intensional data naturally arises in a number of Web data management scenarios, such as Web crawling or ontology-based data access. Such scenarios require us to model an uncertain view of the world, for which, given a query, we must answer the question "What is the best thing to do next?" Once data has been retrieved, the knowledge of the world is revised, and the whole process is repeated, until enough knowledge about the world has been obtained for the particular application considered. In this article, we give an overview of the steps underlying all intensional data management scenarios, and illustrate them on three concrete applications: focused crawling, online influence maximization in social networks, and mining crowdsourced data.

A subjective evidence model for influence maximization in social networks

This paper introduces the notion of subjective evidence, which fuels a new parallel cascade influence propagation model. The model sheds light on the phenomena of belief reinforcement and viral spread of innovations, rumors, opinions, etc., in social networks. Network actors are assumed to be testing a Bayesian hypothesis, e.g., for making judgment about the superiority of some product(s) or service(s) over others, or (dis)utility of a given program/policy. The model-based influence maximization solutions inform the strategies for market niche selection and protection, and identification of susceptible groups in political campaigning. The NP-Hard problem of influential seed selection is first solved as a mixed-integer program. Second, an efficient Lagrangian Relaxation heuristic with guaranteed bounds is presented. In small, medium and large-scale computational investigations, we analyze: (1) how the success of an influence cascade triggered in a (sub)community, long exposed to an opposite belief, depends on the structural properties of the underlying social network, (2) to what extent growing (increasing the density of) a consumer network within a market niche helps a company protect the niche, (3) given a competitor׳s strength, when a company should counter the competitor on “their turf”, and when and how it should look for limited-time opportunities to maximally profit before eventually surrendering the market.