Influence Propagation In Social Networks Research Articles

Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation

In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The popularity ratio is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate Popularity Ratio Maximization (PRM) as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that our proposed method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.

Dynamically aggregating individuals’ social influence and interest evolution for group recommendations

The rapid development of social networks has made group activities part of people’s daily lives. It is in high demand to recommend items for a group of users that meet their common preferences, known as group recommendations (GRs). Recently, some studies have focused on GRs and adopted deep neural networks to model the process of group decision-making. These approaches can be further divided into two phases: the individual preference learning phase and the group decision phase, both of which face several challenges although recent research has some achievements. In the first individual preference learning phase, the challenges are how to effectively incorporate social networks to overcome the sparsity issue of user interaction data and how to capture the dynamic evolutions of users’ preferences over time. The challenge for the second group decision phase is how to adjust group members’ preference weights dynamically since each group member may carry different deciding power in different situations, depending on their roles in the group and domain expertise. In this paper, to address these challenges, we propose a novel group recommendation framework that dynamically aggregates the social influence diffusion and interest evolution (DASIIE). Specifically, DASIIE is composed of three main modules: 1) a social influence diffusion (SID) module which stacks multi-layer graph neural networks to simulate the influence propagation in social networks; 2) an interest evolution learning (IEL) component which learns individuals’ interest evolutions over time from their behavior sequences; 3) a two-way attention (TWA) module which learns group members’ role weights and expertise weights during the decision-making process simultaneously. We conduct extensive experiments on two real-world datasets, and the results show the better performance of our model compared to the state-of-the-art baseline models.

Launcher nodes for detecting efficient influencers in social networks

Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate.This paper proposes a new index for analyzing message propagation in social networks, based on the network topological nature and the influential power of the message. The new index characterizes the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. This division is most evident when the viral power of the message is high. Together with other known metrics, launcher individuals can assist to select efficient influencers in a social network. For instance, instead of choosing a strong member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and then look for the lowest degree members contained therein. These members are probably cheaper (on financial incentives) but still guarantying almost the same influence effectiveness as the largest degree members.We discuss this index using a number of real-world social networks available in known datasets repositories.

An evolutionary framework for maximizing influence propagation in social networks

Abstract Social networks are one the main sources of information transmission nowadays. However, not all nodes in social networks are equal: in fact, some nodes are more influential than others, i.e., their information tends to spread more. Finding the most influential nodes in a network – the so-called Influence Maximization problem – is an NP-hard problem with great social and economical implications. Here, we introduce a framework based on Evolutionary Algorithms that includes various graph-aware techniques (spread approximations, domain-specific operators, and node filtering) that facilitate the optimization process. The framework can be applied straightforwardly to various social network datasets, e.g., those in the SNAP repository.

CSIP: Enhanced Link Prediction with Context of Social Influence Propagation

Data mining in social networks brings an indispensable role for the construction of smart cities from the perspective of social development. Link prediction is an important task of data mining, especially in the knowledge graph, which is also called knowledge graph completion. Link prediction aims to find missing links or predict potential links according to the current social network. The most existing link prediction methods focus on static information in social networks, such as topology and node attributes, which are partly provided by users. When users are unwilling to provide or intentionally hide these static features, traditional link prediction methods cannot achieve ideal performance. The dynamic information of social influence propagation in social networks can avoid the user's subjective impact and better reflect the relationship between users. In addition, users show different degrees of interest and authority on various topics in the real world, leading to different influence propagation patterns. Therefore, we use context of social influence to optimize the topic-aware influence propagation model to improve the performance of link prediction. In this paper, we propose a new multi-output graph neural network framework to capture influence propagation in social networks and model the influence of users in different roles. In this way, the underlying information of influence between users can be used to construct new features to improve the performance of link prediction. Our experiments conduct the method on multiple benchmark datasets. The experimental results show that the modeling of context is effective, and our model outperforms the compared state-of-the-art link prediction methods.

Containment of rumor spread by selecting immune nodes in social networks.

With the popularity of online social network these have become important platforms for the spread of information. This not only includes correct and useful information, but also false information, and even rumors which could result in panic. Therefore, the containment of rumor spread in social networks is important. In this paper, we propose an effective method that involves selecting a set of nodes in (k, η)-cores and immunize these nodes for rumor containment. First, we study rumor influence propagation in social networks under the extended Independent Cascade (EIC) model, an extension of the classic Independent Cascade (IC) model. Then, we decompose a social network into subgraphs via core decomposition of uncertain graphs and compute the number of immune nodes in each subgraph. Further we greedily select nodes with the Maximum Marginal Covering Neighbors Set as immune nodes. Finally, we conduct experiments using real-world datasets to evaluate our method. Experimental results show the effectiveness of our method.

Influence propagation in social networks: Interest-based community ranking model

The driving force behind content dissemination in Social network (SN) is the users’ interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic user clusters “interest groups”.The objective of this work is to rank interest-based communities using influence propagation. The contribution of this work is threefold: First, to highlight the significance of the indirect influence among interest-based user groups. Second, to study its impact on content dissemination capability. Third, to propose an ultimate ranking model (UltRank) that uniquely considers direct and indirect influences which are reflected in a new reachability metric that considers: 1. Distance among interest groups. 2. Percentage of reachable interest groups. 3. Percentage of reachable nodes.UltRank model has been evaluated in comprehensive experiments. First, clustering quality perspective, the Silhouette coefficient for the identified interest groups is on average 0.996 and the Jaccard coefficient of 97% of different interest groups members equals 0. Second, ranking capability perspective, UltRank model can rank up to 91% of interest groups in SN. Finally, ranking effectiveness perspective, UltRank ranking list has a competing network coverage results against the other benchmark approaches.

Influence propagation: Interest groups and node ranking models

Influence propagation is studied in various contexts with significant practical potential applications such as viral marketing, monitoring people opinions, social psychology analysis and communities discovery. All the previously mentioned applications are concerned about the role played by the user in social network and his/her effect on other users. The current literature lacks approaches that identify influential users in social networks and analyze users ranking with respect to the users interactivity to the disseminated content.The main contribution of this work is to achieve users ranking based on influence propagation in social networks. In order to achieve this goal, two models are proposed. The first model captures interest groups regarding specific disseminated content. The second model is a novel influence propagation model that ranks users in each interest group based on their role in spreading content. Moreover, this model introduces the new concept of ”ultimate observers” to adjust the rank of influential users in each group. Finally, we perform extensive experiments on real datasets to demonstrate the relevance of the proposed models.Both models are evaluated in experimental setup using the following benchmark datasets: Highschool, Email-Eu-core, US Airports, Advogato Trust and Twitter Lists networks. The proposed models are assessed in respect of the accurate separation of interest groups, distinction, uniqueness and effectiveness of nodes ranking.Experiments show that the proposed models have promising results in detecting the interest groups and ranking users in terms of their influence propagation.

A fuzzy logic approach to influence maximization in social networks

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

Minimizing the Influence Propagation in Social Networks for Linear Threshold Models

Innovation or information propagation in social networks has been widely studied in recent years. Most of the previous works are focused on solving the problem of influence maximization, which aims to identify a small subset of early adopters in a social network to maximize the influence propagation under a given diffusion model. In this paper, motivated by practical scenarios, we propose two different influence minimization problems. We consider a Linear Threshold diffusion model and provide a general solution to the first problem solving a linear integer programming. For the second problem, we provide a technique to search for an optimal solution that works only in particular cases and discuss a simple heuristic to find a solution in the general case.

Modelling Influence Propagation in Social Networks

This paper presents a formalised description of the models of influence propagation in social networks introduced in the classic paper of Kempe et al. The formal framework that we propose clarifies the structure of the most popular propagation models and helps rigorously re-establish the essential results concerning the problem of influence maximisation. We also introduce new models of propagation and show how they fit into the general picture. In particular, we focus on models that capture either positive or negative effects of resisting influence on individual’s future resistance.

Information and Influence Propagation in Social Networks

Information and Influence Propagation in Social Networks

On minimizing budget and time in influence propagation over social networks

In recent years, study of influence propagation in social networks has gained tremendous attention. In this context, we can identify three orthogonal dimensions—the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagation. We can constrain one or two of these and try to optimize the third. In their seminal paper, Kempe et al. constrained the budget, left time unconstrained, and maximized the coverage: this problem is known as Influence Maximization (or MAXINF for short). In this paper, we study alternative optimization problems which are naturally motivated by resource and time constraints on viral marketing campaigns. In the first problem, termed minimum target set selection (or MINTSS for short), a coverage threshold η is given and the task is to find the minimum size seed set such that by activating it, at least η nodes are eventually activated in the expected sense. This naturally captures the problem of deploying a viral campaign on a budget. In the second problem, termed MINTIME, the goal is to minimize the time in which a predefined coverage is achieved. More precisely, in MINTIME, a coverage threshold η and a budget threshold k are given, and the task is to find a seed set of size at most k such that by activating it, at least η nodes are activated in the expected sense, in the minimum possible time. This problem addresses the issue of timing when deploying viral campaigns. Both these problems are NP-hard, which motivates our interest in their approximation. For MINTSS, we develop a simple greedy algorithm and show that it provides a bicriteria approximation. We also establish a generic hardness result suggesting that improving this bicriteria approximation is likely to be hard. For MINTIME, we show that even bicriteria and tricriteria approximations are hard under several conditions. We show, however, that if we allow the budget for number of seeds k to be boosted by a logarithmic factor and allow the coverage to fall short, then the problem can be solved exactly in PTIME, i.e., we can achieve the required coverage within the time achieved by the optimal solution to MINTIME with budget k and coverage threshold η. Finally, we establish the value of the approximation algorithms, by conducting an experimental evaluation, comparing their quality against that achieved by various heuristics.