On Modeling Influence Maximization in Social Activity Networks under General Settings

2021 ◽  
Vol 15 (6) ◽  
pp. 1-28
Author(s):  
Rui Wang ◽  
Yongkun Li ◽  
Shuai Lin ◽  
Hong Xie ◽  
Yinlong Xu ◽  
...  
Keyword(s):  
Online Social Networks ◽  
Social Activity ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Computationally Efficient ◽  
Activity Networks ◽  
Online Activities ◽  
Influential Users ◽  
Real World Datasets ◽  
The Impact

Finding the set of most influential users in online social networks (OSNs) to trigger the largest influence cascade is meaningful, e.g., companies may leverage the “word-of-mouth” effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various online activities, e.g., joining discussion groups and commenting on same pages or products, influence diffusion through online activities becomes even more significant. In this article, we study the impact of online activities by formulating social-activity networks which contain both users and online activities, and thus induce two types of weighted edges, i.e., edges between users and edges between users and activities. To address the computation challenge, we define an influence centrality via random walks, and use the Monte Carlo framework to efficiently estimate the centrality. Furthermore, we develop a greedy-based algorithm with novel optimizations to find the most influential users for node recommendation. Experiments on real-world datasets show that our approach is very computationally efficient under different influence models, and also achieves larger influence spread by considering online activities.

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

scholarly journals Outbreak minimization v.s. influence maximization: an optimization framework

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chun-Hung Cheng ◽  
Yong-Hong Kuo ◽  
Ziye Zhou
Keyword(s):  
Diffusion Model ◽  
Large Scale ◽  
Benders Decomposition ◽  
Computational Study ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Linear Threshold ◽  
The Impact ◽  
Influence Maximization Problem

Abstract Background An effective approach to containing epidemic outbreaks (e.g., COVID-19) is targeted immunization, which involves identifying “super spreaders” who play a key role in spreading disease over human contact networks. The ultimate goal of targeted immunization and other disease control strategies is to minimize the impact of outbreaks. It shares similarity with the famous influence maximization problem studied in the field of social network analysis, whose objective is to identify a group of influential individuals to maximize the influence spread over social networks. This study aims to establish the equivalence of the two problems and develop an effective methodology for targeted immunization through the use of influence maximization. Methods We present a concise formulation of the targeted immunization problem and show its equivalence to the influence maximization problem under the framework of the Linear Threshold diffusion model. Thus the influence maximization problem, as well as the targeted immunization problem, can be solved by an optimization approach. A Benders’ decomposition algorithm is developed to solve the optimization problem for effective solutions. Results A comprehensive computational study is conducted to evaluate the performance and scalability of the optimization approach on real-world large-scale networks. Computational results show that our proposed approaches achieve more effective solutions compared to existing methods. Conclusions We show the equivalence of the outbreak minimization and influence maximization problems and present a concise formulation for the influence maximization problem under the Linear Threshold diffusion model. A tradeoff between computational effectiveness and computational efficiency is illustrated. Our results suggest that the capability of determining the optimal group of individuals for immunization is particularly crucial for the containment of infectious disease outbreaks within a small network. Finally, our proposed methodology not only determines the optimal solutions for target immunization, but can also aid policymakers in determining the right level of immunization coverage.

scholarly journals Context Attention Heterogeneous Network Embedding

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Wei Zhuo ◽  
Qianyi Zhan ◽  
Yuan Liu ◽  
Zhenping Xie ◽  
Jing Lu
Keyword(s):  
Real World ◽  
Online Social Networks ◽  
Heterogeneous Network ◽  
Network Embedding ◽  
Node Importance ◽  
Unweighted Network ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Types Of Information ◽  
The Impact

Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimensional embedding space due to heterogeneity. Besides, in the unweighted network, how to quantify the strength of edges (tightness of connections between nodes) accurately is also a difficulty faced by existing methods. To bridge the gap, in this paper, we propose CAHNE (context attention heterogeneous network embedding), a novel network embedding method, to accurately determine the learning result. Specifically, we propose the concept of node importance to measure the strength of edges, which can better preserve the context relations of a node in unweighted networks. Moreover, text information is a widely ubiquitous feature in real-world networks, e.g., online social networks and citation networks. On account of the sophisticated interactions between the network structure and text features of nodes, CAHNE learns context embeddings for nodes by introducing the context node sequence, and the attention mechanism is also integrated into our model to better reflect the impact of context nodes on the current node. To corroborate the efficacy of CAHNE, we apply our method and various baseline methods on several real-world datasets. The experimental results show that CAHNE achieves higher quality compared to a number of state-of-the-art network embedding methods on the tasks of network reconstruction, link prediction, node classification, and visualization.

Modeling Personality Effect in Trust Prediction

2021 ◽  
Vol 2 (4) ◽  
pp. 401-417
Author(s):  
Seyed M. Ghafari ◽  
Amin Beheshti ◽  
Aditya Joshi ◽  
Cecile Paris ◽  
Shahpar Yakhchi ◽  
...  
Keyword(s):  
Social Networks ◽  
Personality Traits ◽  
Online Social Networks ◽  
Tensor Decomposition ◽  
Low Rank ◽  
Superior Performance ◽  
Trust Prediction ◽  
Key Factor ◽  
Real World Datasets ◽  
The Impact

Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.

scholarly journals A quick GRASP-based method for influence maximization in social networks

2021 ◽  
Author(s):  
Isaac Lozano-Osorio ◽  
Jesús Sánchez-Oro ◽  
Abraham Duarte ◽  
Óscar Cordón
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Search ◽  
Computing Time ◽  
Exact Algorithm ◽  
Influence Maximization ◽  
Computational Effort ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influential Users

AbstractThe evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$ NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

scholarly journals On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaodong Liu ◽  
Xiangke Liao ◽  
Shanshan Li ◽  
Si Zheng ◽  
Bin Lin ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
Search Space ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Dynamic Social Networks ◽  
Influence Spread ◽  
The Impact ◽  
Influential Individuals

Influence maximization problem aims to identify the most influential individuals so as to help in developing effective viral marketing strategies over social networks. Previous studies mainly focus on designing efficient algorithms or heuristics on a static social network. As a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time. In this paper, we propose an incremental approach, IncInf, which can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to narrow the search space into nodes experiencing major increases or with high degrees. To evaluate the efficiency and effectiveness, we carried out extensive experiments on real-world dynamic social networks: Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static algorithm, IncInf achieves remarkable speedup in execution time while maintaining matching performance in terms of influence spread.

Predicting Influential Users in Online Social Network Groups

2021 ◽  
Vol 15 (3) ◽  
pp. 1-50
Author(s):  
Andrea De Salve ◽  
Paolo Mori ◽  
Barbara Guidi ◽  
Laura Ricci ◽  
Roberto Di Pietro
Keyword(s):  
Online Social Networks ◽  
Online Social Network ◽  
Research Perspective ◽  
Ex Post ◽  
Time Period ◽  
Future Time Period ◽  
Academic Researchers ◽  
Influential Users ◽  
Analytical Tools ◽  
Share Information

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

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