scholarly journals Influence Maximization with Priority in Online Social Networks

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 183
Author(s):  
Canh V. Pham ◽  
Dung K. T. Ha ◽  
Quang C. Vu ◽  
Anh N. Su ◽  
Huan X. Hoang
Keyword(s):  
Social Network ◽  
Online Social Networks ◽  
Sampling Method ◽  
State Of The Art ◽  
Influence Maximization ◽  
Approximation Solution ◽  
Worst Case ◽  
New Approach ◽  
Influence Spread ◽  
Seed Collections

The Influence Maximization (IM) problem, which finds a set of k nodes (called seedset) in a social network to initiate the influence spread so that the number of influenced nodes after propagation process is maximized, is an important problem in information propagation and social network analysis. However, previous studies ignored the constraint of priority that led to inefficient seed collections. In some real situations, companies or organizations often prioritize influencing potential users during their influence diffusion campaigns. With a new approach to these existing works, we propose a new problem called Influence Maximization with Priority (IMP) which finds out a set seed of k nodes in a social network to be able to influence the largest number of nodes subject to the influence spread to a specific set of nodes U (called priority set) at least a given threshold T in this paper. We show that the problem is NP-hard under well-known IC model. To find the solution, we propose two efficient algorithms, called Integrated Greedy (IG) and Integrated Greedy Sampling (IGS) with provable theoretical guarantees. IG provides a 1−(1−1k)t-approximation solution with t is an outcome of algorithm and t≥1. The worst-case approximation ratio is obtained when t=1 and it is equal to 1/k. In addition, IGS is an efficient randomized approximation algorithm based on sampling method that provides a 1−(1−1k)t−ϵ-approximation solution with probability at least 1−δ with ϵ>0,δ∈(0,1) as input parameters of the problem. We conduct extensive experiments on various real networks to compare our IGS algorithm to the state-of-the-art algorithms in IM problem. The results indicate that our algorithm provides better solutions interns of influence on the priority sets when approximately give twice to ten times higher than threshold T while running time, memory usage and the influence spread also give considerable results compared to the others.

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Guanhao Wu ◽  
Xiaofeng Gao ◽  
Ge Yan ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Parallel Algorithms ◽  
Real World ◽  
Online Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Memory Overhead ◽  
Influence Spread ◽  
Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

scholarly journals Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

2021 ◽  
Author(s):  
VIMAL KUMAR P. ◽  
Balasubramanian C.
Keyword(s):  
Social Network ◽  
Online Social Networks ◽  
Large Scale ◽  
Information Dissemination ◽  
Influence Maximization ◽  
Centrality Measures ◽  
Least Angle Regression ◽  
Influence Spread ◽  
Katz Centrality ◽  
Influential Node

Abstract With the epidemic growth of online social networks (OSNs), a large scale research on information dissemination in OSNs has been made an appearance in contemporary years. One of the essential researches is influence maximization (IM). Most research adopts community structure, greedy stage, and centrality measures, to identify the influence node set. However, the time consumed in analyzing the influence node set for edge server placement, service migration and service recommendation is ignored in terms of propagation delay. Considering the above analysis, we concentrate on the issue of time-sensitive influence maximization and maximize the targeted influence spread. To solve the problem, we propose a method called, Trilateral Spearman Katz Centrality-based Least Angle Regression (TSKC-LAR) for influential node tracing in social network is proposed. Besides, two algorithms are used in our work to find the influential node in social network with maximum influence spread and minimal time, namely Trilateral Statistical Node Extraction algorithm and Katz Centrality Least Angle Influence Node Tracing algorithm, respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics, namely, sensitivity, specificity, accuracy, time and influence spread

scholarly journals One-Shot Neural Architecture Search via Novelty Driven Sampling

2020 ◽  
Author(s):  
Miao Zhang ◽  
Huiqi Li ◽  
Shirui Pan ◽  
Taoping Liu ◽  
Steven Su
Keyword(s):  
Sampling Method ◽  
State Of The Art ◽  
Bilevel Optimization ◽  
Test Accuracy ◽  
New Approach ◽  
Neural Architecture ◽  
Novelty Search ◽  
Effectiveness And Efficiency ◽  
The Rich ◽  
Single Path

One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming this validation accuracy approximates to the test accuracy after re-training. However, recent works have found that there is no positive correlation between the above validation accuracy and test accuracy for these One-Shot NAS methods, and this reward based sampling for supernet training also entails the rich-get-richer problem. To handle this deceptive problem, this paper presents a new approach, Efficient Novelty-driven Neural Architecture Search, to sample the most abnormal architecture to train the supernet. Specifically, a single-path supernet is adopted, and only the weights of a single architecture sampled by our novelty search are optimized in each step to reduce the memory demand greatly. Experiments demonstrate the effectiveness and efficiency of our novelty search based architecture sampling method.

Improvements to Ullmann's Algorithm for the Subgraph Isomorphism Problem

2015 ◽  
Vol 29 (07) ◽  
pp. 1550025 ◽  
Author(s):  
Uroš Čibej ◽  
Jurij Mihelič
Keyword(s):  
Pattern Recognition ◽  
Social Network ◽  
Network Analysis ◽  
State Of The Art ◽  
Isomorphism Problem ◽  
Exact Algorithms ◽  
Subgraph Isomorphism ◽  
Worst Case ◽  
Time Consumption ◽  
Space Requirements

The subgraph isomorphism problem is one of the most important problems for pattern recognition in graphs. Its applications are found in many different disciplines, including chemistry, medicine, and social network analysis. Because of the [Formula: see text]-completeness of the problem, the existing exact algorithms exhibit an exponential worst-case running time. In this paper, we propose several improvements to the well-known Ullmann's algorithm for the problem. The improvements lower the time consumption as well as the space requirements of the algorithm. We experimentally demonstrate the efficiency of our improvement by comparing it to another set of improvements called FocusSearch, as well as other state-of-the-art algorithms, namely VF2 and LAD.

scholarly journals Influence Maximization Based on Backward Reasoning in Online Social Networks

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3189
Author(s):  
Lin Zhang ◽  
Kan Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Online Social Network ◽  
Rapid Development ◽  
Influence Maximization ◽  
Research Issues ◽  
Backward Reasoning ◽  
New Framework

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising.

scholarly journals Scalable Rule Learning via Learning Representation

2018 ◽  
Author(s):  
Pouya Ghiasnezhad Omran ◽  
Kewen Wang ◽  
Zhe Wang
Keyword(s):  
Link Prediction ◽  
Sampling Method ◽  
State Of The Art ◽  
Rule Learning ◽  
Representation Learning ◽  
Data Repositories ◽  
New Approach ◽  
First Order ◽  
Learning Rules ◽  
Recent Advancement

We study the problem of learning first-order rules from large Knowledge Graphs (KGs). With recent advancement in information extraction, vast data repositories in the KG format have been obtained such as Freebase and YAGO. However, traditional techniques for rule learning are not scalable for KGs. This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. Experimental results show that our system outperforms some state-of-the-art systems. Specifically, for massive KGs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in KGs such as AMIE+. We also used the RLvLR-mined rules in an inference module to carry out the link prediction task. In this task, RLvLR outperformed Neural LP, a state-of-the-art link prediction system, in both runtime and accuracy.

Taxonomy of Influence Maximization Techniques in Unknown Social Networks

2021 ◽  
pp. 351-363
Author(s):  
B. Bazeer Ahamed ◽  
Sudhakaran Periakaruppan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Graph Mining ◽  
Influence Maximization ◽  
Business Strategies ◽  
The Social ◽  
Spread Of Influence ◽  
Maximization Algorithms ◽  
Real Challenge

Influence maximization in online social networks (OSNs) is the problem of discovering few nodes or users in the social network termed as ‘seed nodes', which can help the spread of influence in the network. With the tremendous growth in social networking, the influence exerted by users of a social network on other online users has caught the attention of researchers to develop effective influence maximization algorithms to be applied in the field of business strategies. The main application of influence maximization is promoting the product to a set of users. However, a real challenge in influence maximization algorithms to deal with enormous amount of users or nodes obtainable in any OSN is posed. The authors focused on graph mining of OSNs for generating ‘seed sets' using standard influence maximization techniques. Many standard influence maximization models are used for calculation of spread of influence; a novel influence maximization technique, namely the DegGreedy technique, has been illustrated along with experimental results to make a comparative analysis of the existing techniques.

scholarly journals Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

2020 ◽  
Vol 12 (9) ◽  
pp. 148 ◽  
Author(s):  
Max Ismailov ◽  
Michail Tsikerdekis ◽  
Sherali Zeadally
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Online Social Networks ◽  
Laboratory Experiments ◽  
Deception Detection ◽  
State Of The Art ◽  
Online Social Network ◽  
Ongoing Research ◽  
The Real

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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