Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks

Xuli Rao; Jiaxu Zhao; Zhide Chen; Feng Lin

doi:10.3390/fi11050112

Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks

Future Internet ◽

10.3390/fi11050112 ◽

2019 ◽

Vol 11 (5) ◽

pp. 112 ◽

Cited By ~ 1

Author(s):

Xuli Rao ◽

Jiaxu Zhao ◽

Zhide Chen ◽

Feng Lin

Keyword(s):

Social Networks ◽

Influence Maximization ◽

Mining Algorithm ◽

Simulation Results ◽

Low Performance ◽

Initial Seed ◽

And Performance ◽

Influence Spreading ◽

Mining Algorithms ◽

Critical Challenge

Due to the growing interconnections of social networks, the problem of influence maximization has been extended from a single social network to multiple social networks. However, a critical challenge of influence maximization in multi-social networks is that some initial seed nodes may be unable to be active, which obviously leads to a low performance of influence spreading. Therefore, finding substitute nodes for mitigating the influence loss of uncooperative nodes is extremely helpful in influence maximization. In this paper, we propose three substitute mining algorithms for influence maximization in multi-social networks, namely for the Greedy-based substitute mining algorithm, pre-selected-based substitute mining algorithm, and similar-users-based substitute mining algorithm. The simulation results demonstrate that the existence of the uncooperative seed nodes leads to the range reduction of information influence. Furthermore, the viability and performance of the proposed algorithms are presented, which show that three substitute node mining algorithms can find suitable substitute nodes for multi-social networks influence maximization, thus achieves better influence.

Download Full-text

Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union

PLoS ONE ◽

10.1371/journal.pone.0256329 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0256329

Author(s):

Rory Bunker ◽

Keisuke Fujii ◽

Hiroyuki Hanada ◽

Ichiro Takeuchi

Keyword(s):

Pattern Mining ◽

Sequential Pattern Mining ◽

Rugby Union ◽

Sequential Pattern ◽

Frequent Patterns ◽

Event Sequences ◽

Mining Algorithm ◽

Original Dataset ◽

And Performance ◽

Mining Algorithms

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

Download Full-text

Location-sharing protocol for privacy protection in mobile online social networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01999-z ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Ou Ruan ◽

Lixiao Zhang ◽

Yuanyuan Zhang

Keyword(s):

Social Networks ◽

Private Information ◽

Online Social Networks ◽

Smart Cities ◽

Location Based Services ◽

Broadcast Encryption ◽

Location Sharing ◽

Identity Privacy ◽

And Performance ◽

Network Server

AbstractLocation-based services are becoming more and more popular in mobile online social networks (mOSNs) for smart cities, but users’ privacy also has aroused widespread concern, such as locations, friend sets and other private information. At present, many protocols have been proposed, but these protocols are inefficient and ignore some security risks. In the paper, we present a new location-sharing protocol, which solves two issues by using symmetric/asymmetric encryption properly. We adopt the following methods to reduce the communication and computation costs: only setting up one location server; connecting social network server and location server directly instead of through cellular towers; avoiding broadcast encryption. We introduce dummy identities to protect users’ identity privacy, and prevent location server from inferring users’ activity tracks by updating dummy identities in time. The details of security and performance analysis with related protocols show that our protocol enjoys two advantages: (1) it’s more efficient than related protocols, which greatly reduces the computation and communication costs; (2) it satisfies all security goals; however, most previous protocols only meet some security goals.

Download Full-text

Link Prediction-Based Influence Maximization in Online Social Networks

Neurocomputing ◽

10.1016/j.neucom.2021.04.084 ◽

2021 ◽

Author(s):

Ashwini Kumar Singh ◽

Lakshmanan Kailasam

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Link Prediction ◽

Influence Maximization

Download Full-text

Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

Symmetry ◽

10.3390/sym13020208 ◽

2021 ◽

Vol 13 (2) ◽

pp. 208

Author(s):

Qifei Zhou ◽

Changqing Zhu ◽

Na Ren ◽

Weitong Chen ◽

Weiteng Gong

Keyword(s):

Second Order ◽

Superior Performance ◽

Statistical Characteristics ◽

First Order ◽

Geographic Data ◽

Simulation Results ◽

Topological Characteristics ◽

Zero Watermarking ◽

Critical Challenge

Zero watermarking is an important part of copyright protection of vector geographic data. However, how to improve the robustness of zero watermarking is still a critical challenge, especially in resisting attacks with significant distortion. We proposed a zero watermarking method for vector geographic data based on the number of neighboring features. The method makes full use of spatial characteristics of vector geographic data, including topological characteristics and statistical characteristics. First, the number of first-order neighboring features (NFNF) and the number of second-order neighboring features (NSNF) of every feature in vector geographic data are counted. Then, the watermark bit is determined by the NFNF value, and the watermark index is determined by the NSNF value. Finally, combine the watermark bits and the watermark indices to construct a watermark. Experiments verify the theoretical achievements and good robustness of this method. Simulation results also demonstrate that the normalized coefficient of the method is always kept at 1.00 under the attacks that distort data significantly, which has the superior performance in comparison to other methods.

Download Full-text

The effect of social distancing on the reach of an epidemic in social networks

Journal of Economic Interaction and Coordination ◽

10.1007/s11403-021-00322-9 ◽

2021 ◽

Author(s):

Gregory Gutin ◽

Tomohiro Hirano ◽

Sung-Ha Hwang ◽

Philip R. Neary ◽

Alexis Akira Toda

Keyword(s):

Public Health ◽

Infectious Disease ◽

Social Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Disease Transmission ◽

Global Level ◽

Social Distancing ◽

The Public ◽

Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

Download Full-text

Review of short-text classification

International Journal of Web Information Systems ◽

10.1108/ijwis-12-2017-0083 ◽

2019 ◽

Vol 15 (2) ◽

pp. 155-182 ◽

Cited By ~ 5

Author(s):

Issa Alsmadi ◽

Keng Hoon Gan

Keyword(s):

Social Networks ◽

Text Classification ◽

Development Trend ◽

Practical Reasons ◽

Significant Implication ◽

Classification Problems ◽

Content Type ◽

Short Text ◽

Promising Area ◽

Low Performance

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

Download Full-text

Compatible Influence Maximization in Online Social Networks

IEEE Transactions on Computational Social Systems ◽

10.1109/tcss.2021.3064400 ◽

2021 ◽

pp. 1-12

Author(s):

Lei Yu ◽

Guohui Li ◽

Ling Yuan

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Influence Maximization

Download Full-text

Influence Maximization in Multi-Relational Social Networks

10.1145/3459637.3481928 ◽

2021 ◽

Author(s):

Wei Wang ◽

Haili Yang ◽

Yuanfu Lu ◽

Yuanhang Zou ◽

Xu Zhang ◽

...

Keyword(s):

Social Networks ◽

Influence Maximization

Download Full-text

Heterogeneous Influence Maximization Through Community Detection in Social Networks

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021100107 ◽

2021 ◽

Vol 12 (4) ◽

pp. 118-131

Author(s):

Jaya Krishna Raguru ◽

Devi Prasad Sharma

Keyword(s):

Community Detection ◽

Greedy Algorithms ◽

Computational Cost ◽

Optimal Solution ◽

Influence Maximization ◽

Centrality Measures ◽

Influence Spread ◽

Real World Datasets ◽

Initial Seed ◽

High Computational Cost

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

Download Full-text