Seed set selection in evolving social networks

2017 ◽  
Author(s):  
Shuai Xu ◽  
Naiting Xu ◽  
Jiahao Zhang ◽  
Feiyang Li ◽  
Shasha Li
Keyword(s):  
Social Networks ◽  
Seed Set

Positive Influence Maximization in the Signed Social Networks Considering Polarity Relationship and Propagation Probability

2021 ◽  
Vol 31 (02) ◽  
pp. 249-267
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Jinfeng Yu
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Positive Influence ◽  
Vital Role ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Propagation Model ◽  
Maximization Problem ◽  
Signed Social Networks ◽  
Independent Cascade

The purpose of influence maximization problem is to select a small seed set to maximize the number of nodes influenced by the seed set. For viral marketing, the problem of influence maximization plays a vital role. Current works mainly focus on the unsigned social networks, which include only positive relationship between users. However, the influence maximization in the signed social networks including positive and negative relationships between users is still a challenging issue. Moreover, the existing works pay more attention to the positive influence. Therefore, this paper first analyzes the positive maximization influence in the signed social networks. The purpose of this problem is to select the seed set with the most positive influence in the signed social networks. Afterwards, this paper proposes a model that incorporates the state of node, the preference of individual and polarity relationship, called Independent Cascade with the Negative and Polarity (ICWNP) propagation model. On the basis of the ICWNP model, this paper proposes a Greedy with ICWNP algorithm. Finally, on four real social networks, experimental results manifest that the proposed algorithm has higher accuracy and efficiency than the related methods.

Influence maximisation in social networks: A target-oriented estimation

2018 ◽  
Vol 44 (5) ◽  
pp. 671-682 ◽  
Author(s):  
Yun-Yong Ko ◽  
Dong-Kyu Chae ◽  
Sang-Wook Kim
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Seed Set ◽  
Potential Influence ◽  
Influence Spread ◽  
Real World Datasets ◽  
Novel Target ◽  
Individual Node

Influence maximisation (IM) is the problem of finding a set of k-seed nodes that could maximize the amount of influence spread in a social network. In this article, we point out that the existing methods are taking the source-oriented estimation (SOE), which is the main reason of their failure in accurately estimating the amount of potential influence spread of an individual node. We propose a novel target-oriented estimation (TOE) that understands information diffusion more accurately as well as remedies the drawback of the existing methods. Our extensive experiments on four real-world datasets demonstrate that our proposed method outperforms the existing methods consistently with respect to the quality of the selected seed set.

Target users' activation probability maximization with different seed set constraints in social networks

2020 ◽  
Vol 838 ◽  
pp. 111-125
Author(s):  
Ruidong Yan ◽  
Hongwei Du ◽  
Yi Li ◽  
Wenping Chen ◽  
Yongcai Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Set Constraints ◽  
Activation Probability

Net positive influence maximization in signed social networks

2021 ◽  
pp. 1-12
Author(s):  
Dong Li ◽  
Yuejiao Wang ◽  
Muhao Li ◽  
Xin Sun ◽  
Jingchang Pan ◽  
...  
Keyword(s):  
Social Networks ◽  
Greedy Algorithm ◽  
Seed Set ◽  
Social Systems ◽  
Research Direction ◽  
Negative Influence ◽  
Positive Influence ◽  
Influence Maximization ◽  
The Real ◽  
Signed Social Networks

In the real world, a large number of social systems can be modeled as signed social networks including both positive and negative relationships. Influence maximization in signed social networks is an interesting and significant research direction, which has gained some attention. All of existing studies mainly focused on positive influence maximization (PIM) problem. The goal of the PIM problem is to select the seed set with maximum positive influence in signed social networks. However, the selected seed set with maximum positive influence may also has a large amount of negative influence, which will cause bad effects in the real applications. Therefore, maximizing purely positive influence is not the final and best goal in signed social networks. In this paper, we introduce the concept of net positive influence and propose the net positive influence maximization (NPIM) problem for signed social networks, to select the seed set with as much positive influence as possible and as less negative influence as possible. Additionally, we prove that the objective function of NPIM problem under polarity-related independent cascade model is non-monotone and non-submodular, which means the traditional greedy algorithm is not applicable to the NPIM problem. Thus, we propose an improved R-Greedy algorithm to solve the NPIM problem. Extensive experiments on two Epinions and Slashdot datasets indicate the differences between positive influence and net positive influence, and also demonstrate that our proposed solution performs better than the state-of-the-art methods in terms of promoting net positive influence diffusion in less running time.

scholarly journals Influence maximization in social networks

2019 ◽  
Author(s):  
◽  
Ghinwa Bou Matar
Keyword(s):  
Social Networks ◽  
Seed Set ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Influence Propagation ◽  
Influence Model ◽  
Main Challenge ◽  
Influence Spread ◽  
Total Influence

The main challenge in viral marketing, that is powered by social networks, is to minimize the seed set that will initiate the diffusion process and maximize the total influence at its termination. The aim of this thesis is to study influence propagation models under the influence maximization problem and to investigate the effectiveness of a new model that is based on a multi-objective approach. We propose a Depth-Based Diminishing Influence model (DBDM) that is based on adding nodes to the seed set by considering influenced in-neighbors and how far these in-neighbors are from the initial activated set. As an enhancement to our approach, we used a clustering mechanism to help increase the influence spread. Several experiments were conducted to compare between our approach and previous work. As a result, the selection of the seed set under the DBDM model boosted the influence spread substantially compared to previously proposed models.

scholarly journals Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks

2020 ◽  
Vol 9 (3) ◽  
pp. 497
Author(s):  
Arnold Adimabua Ojugo ◽  
Debby Oghenevwede Otakore
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Seed Set ◽  
Effective Communication ◽  
Social Graph ◽  
Creation Process ◽  
Process Dynamic ◽  
Small Seed ◽  
Group Relationships ◽  
The Many

Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.

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