Finding influential users for different time bounds in social networks using multi-objective optimization

Online social networks play an important role in marketing services. Influence maximization is a major challenge, in which the goal is to find the most influential users in a social network. Increasing the number of influenced users at the end of a diffusion process while decreasing the time of diffusion are two main objectives of the influence maximization problem. The goal of this paper is to find multiple sets of influential users such that each of them is the best set to spread influence for a specific time bound. Considering two conflicting objectives, increasing influence and decreasing diffusion time, we employ the NSGA-II algorithm which is a powerful algorithm in multi-objective optimization to find different seed sets with high influence at different diffusion times. Since social networks are large, computing influence and diffusion time of all chromosomes in each iteration will be challenging and computationally expensive. Therefore, we propose two methods which can estimate the expected influence and diffusion time of a seed set in an efficient manner. Providing the set of all potentially optimal solutions helps a decision maker evaluate the trade-offs between the two objectives, i.e., the number of influenced users and diffusion time. In addition, we develop an approach for selecting seed sets, which have optimal influence for specific time bounds, from the resulting Pareto front of the NSGA-II. Finally, we show that applying our algorithm to real social networks outperforms existing algorithms for the influence maximization problem. The results show a good compromise between the two objectives and the final seed sets result in high influence for different time bounds

Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Data Dissemination And Information Diffusion In Social Networks

Data dissemination problem is a challenging issue in social networks, especially in mobile social networks, which grows rapidly in recent years worldwide with a significant increasing number of hand-on mobile devices such as smart phones and pads. Short-range radio communications equipped in mobile devices enable mobile users to access their interested contents not only from access points of Internet but also from other mobile users. Through proper data dissemination among mobile users, the bandwidth of the short-range communications can be better utilized and alleviate the stress on the bandwidth of the cellular networks. In this dissertation proposal, data dissemination problem in mobile social networks is studied. Before data dissemination emerges in the research of mobile social networks, routing protocol of finding efficient routing path in mobile social networks was the focus, which later became the pavement for the study of the efficient data dissemination. Data dissemination priorities on packet dissemination from multiple sources to multiple destinations while routing protocol simply focus on finding routing path between two ends in the networks. The first works in the literature of data dissemination problem were based on the modification and improvement of routing protocols in mobile social networks. Therefore, we first studied and proposed a prediction-based routing protocol in delay tolerant networks. Delay tolerant network appears earlier than mobile social networks. With respect to delay tolerant networks, mobile social networks also consider social patterns as well as mobility patterns. In our work, we simply come up with the prediction-based routing protocol through analysis of user mobility patterns. We can also apply our proposed protocol in mobile social networks. Secondly, in literature, efficient data dissemination schemes are proposed to improve the data dissemination ratio and with reasonable overhead in the networks. However, the overhead may be not well controlled in the existing works. A social-aware data dissemination scheme is proposed in this dissertation proposal to study efficient data dissemination problem with controlled overhead in mobile social networks. The data dissemination scheme is based on the study on both mobility patterns and social patterns of mobile social networks. Thirdly, in real world cases, an efficient data dissemination in mobile social networks can never be realized if mobile users are selfish, which is true unfortunately in fact. Therefore, how to strengthen nodal cooperation for data dissemination is studied and a credit-based incentive data dissemination protocol is also proposed in this dissertation. Data dissemination problem was primarily researched on mobile social networks. When consider large social networks like online social networks, another similar problem was researched, namely, information diffusion problem. One specific problem is influence maximization problem in online social networks, which maximize the result of information diffusion process. In this dissertation proposal, we proposed a new information diffusion model, namely, sustaining cascading (SC) model to study the influence maximization problem and based on the SC model, we further plan our research work on the information diffusion problem aiming at minimizing the influence diffusion time with subject to an estimated influence coverage

Efektivní algoritmy pro problémy se sociálním vlivem u velkých sítí

In recent years, the dizzying explosion of data and information results from social networks with millions to billions of users, such as Facebook, YouTube, Twitter, and LinkedIn. Users can use online social networks (OSNs) to quickly trade information, communicate with other users, and keep their information up-to-date. The challenge of spreading information on social networks that arises in practice requires effective information management solutions, such as disseminating useful information, maximizing the influence of information transmission, and preventing disinformation, rumors, and viruses from being disseminated. Motivated by the above issues, we investigate the problem of information diffusion on OSNs. We study this problem based on two models, Independent Cascade (IC) and Linear Threshold (LT), and classical Influence Maximization (IM) in online social networks. In addition, we investigate various aspects of IM problems, such as budget variations, topics of interest, multiple competitors, and others. Moreover, we also investigate and apply the theory of combinatorial optimization problems to solve one of the current concerns in social networks, maximizing the influence on the groups and topics in social networks. In general, the main goals of the Ph.D thesis proposal are as follows. 1. We investigate the Multi-Threshold problem for IM, which is a variant of the IM problem with threshold constraints. We propose an efficient algorithm that IM for multiple thresholds in the social network. In particular, we develop a novel algorithmic framework that can use the solution to a smaller threshold to find that of larger ones. 2. We study the Group Influence Maximization problem and introduce an efficient group influence maximization algorithm with more advantages than each node’s influence in networks, using a novel sampling technique to estimate the epsilon group function. We also devised an approximation algorithm to estimate multiple candidate solutions with theoretical guarantee. 3. We investigate an approach for Influence Maximization problem with k-topic under constraints in social network. More specifically, we also study a streaming algorithm that combines an optimization algorithm to improve the approximation algorithm and theoretical guarantee in terms of solution quality and running time.V posledních letech je závratná exploze dat a informací výsledkem sociálních sítí s miliony až miliardami uživatelů, jako jsou Facebook, YouTube, Twitter a LinkedIn. Uživatelé mohou využívat online sociální sítě (OSNs) k rychlému obchodování s informacemi, komunikaci s ostatními uživateli a udržování jejich informací v aktuálním stavu. Výzva šíření informací na sociálních sítích, která se v praxi objevuje, vyžaduje efektivní řešení správy informací, jako je šíření užitečných informací, maximalizace vlivu přenosu informací a zabránění šíření dezinformací, fám a virů. Motivováni výše uvedenými problémy zkoumáme problém šíření informací na OSN. Tento problém studujeme na základě dvou modelů, Independent Cascade (IC) a Linear Threshold (LT) a klasické Influence Maximization (IM) v online sociálních sítích. Kromě toho zkoumáme různé aspekty problémů s rychlým zasíláním zpráv, jako jsou změny rozpočtu, témata zájmu, více konkurentů a další. Kromě toho také zkoumáme a aplikujeme teorii kombinatorických optimalizačních problémů k vyřešení jednoho ze současných problémů v sociálních sítích, maximalizujeme vliv na skupiny a témata v sociálních sítích. Obecně lze říci, že hlavní cíle Ph.D. návrh diplomové práce je následující. 1. Zkoumáme problém Multi-Threshold pro IM, což je varianta problému IM s prahovými omezeními. Navrhujeme účinný algoritmus, který IM pro více prahů v sociální síti. Zejména vyvíjíme nový algoritmický rámec, který může použít řešení pro menší práh k nalezení prahu většího. 2. Studujeme problém maximalizace vlivu skupiny a zavádíme účinný algoritmus maxima- lizace vlivu skupiny s více výhodami, než je vliv každého uzlu v sítích, pomocí nové vzorkovací techniky k odhadu funkce skupiny epsilon. Navrhujeme také aproximační algoritmus pro odhad více kandidátních řešení s teoretickou zárukou. 3. Zkoumáme přístup pro maximalizaci vlivu s k-téma pod omezeními v rozsáhlé síti. Konkrétněji budeme studovat novou metriku, která kombinuje optimalizační algoritmus pro zlepšení aproximačního algoritmu z hlediska kvality řešení a doby běhu na základě kliky a komunity v komplexních sítích.460 - Katedra informatikyvyhově

Online and Offline Dynamic Influence Maximization Games Over Social Networks

In this work, we consider dynamic influence maximization games over social networks with multiple players (influencers). The goal of each influencer is to maximize their own reward subject to their limited total budget rate constraints. Thus, influencers need to carefully design their investment policies considering individuals' opinion dynamics and other influencers' investment strategies, leading to a dynamic game problem. We first consider the case of a single influencer who wants to maximize its utility subject to a total budget rate constraint. We study both offline and online versions of the problem where the opinion dynamics are either known or not known a priori. In the singe-influencer case, we propose an online no-regret algorithm, meaning that as the number of campaign opportunities grows, the average utilities obtained by the offline and online solutions converge. Then, we consider the game formulation with multiple influencers in offline and online settings. For the offline setting, we show that the dynamic game admits a unique Nash equilibrium policy and provide a method to compute it. For the online setting and with two influencers, we show that if each influencer applies the same no-regret online algorithm proposed for the single-influencer maximization problem, they will converge to the set of $\epsilon$-Nash equilibrium policies where $\epsilon=O(\frac{1}{\sqrt{K}})$ scales in average inversely with the number of campaign times $K$ considering the average utilities of the influencers. Moreover, we extend this result to any finite number of influencers under more strict requirements on the information structure. Finally, we provide numerical analysis to validate our results under various settings.Comment: This work has been submitted to IEEE for possible publicatio