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Benefits of Bias in Crawl-Based Network Sampling for Identifying Key Node Set

IEEE ACCESS 2020年 8卷 75370-75380页

作者： Tsugawa, Sho Ohsaki, Hiroyuki Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki 3058573 Japan Kwansei Gakuin Univ Sch Sci & Technol Sanda 6691337 Japan

We study the problem of identifying a set of key nodes from a network when limited knowledge about its structure is available. Most studies assume complete knowledge of the given network when identifying a set of key nodes, but in current practice, networks of interest are often too huge to obtain their entire topological structures. When the complete structure of a network is not available, network sampling strategies are often used to obtain a partial structure of the network. We investigate how network sampling strategies affect the problem of identifying a key node set. Specifically, we investigate the effect of conventional network sampling strategies on the solutions found for two types of key node set identification problems: the minimum p-median problem and the influence maximization problem. Our results show that when the network is obtained using crawl-based network sampling strategies, both the minimum pmedian and the influence maximization problems are effectively solved by simple heuristic algorithms with sampling ratios in the 10-20% range. We also find that among three conventional sampling strategies (random sampling, random walk sampling, and sample edge counts) checked in this paper, random walk sampling is the most robust strategy in terms of effectively identifying the key node sets of diverse types of networks.

关键词： influence maximization problem key node set identification minimum p-median problem network sampling social networks

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influence maximization algorithm based on reducing search space in the social networks

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SN APPLIED SCIENCES 2020年第12期2卷 1-14页

作者： Aghaee, Zahra Kianian, Sahar Shahid Rajaee Teacher Training Univ SRTTU Fac Comp Engn Tehran Iran

The influence maximization problem in social networks is an optimization problem in viral marketing. This problem is concerned with identifying a certain number of people with the most influence in the social network level. Considering the NP-hard of this problem, finding an optimal solution with acceptable accuracy and the low running time is of the high importance. To this purpose, GIN (Group of Influential Nodes) algorithm is presented in this article which creates different groups of graph nodes with more connections than other groups. Then, it selects specific nodes from each group to reduce the search space to find the most influential nodes. Following the greedy method, it selects the seed nodes with the highest expected diffusion value. Experimental results show that the GIN algorithm has provided high influence spread along with low running time in comparison algorithms on all seven real-world datasets.

关键词： Social network influence maximization problem Viral marketing Information diffusion

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Efficient influence spread estimation for influence maximization

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SOCIAL NETWORK ANALYSIS AND MINING 2020年第1期10卷 1-21页

作者： Aghaee, Zahra Kianian, Sahar Shahid Rajaee Teacher Training Univ Dept Comp Sci Tehran Iran

Word-of-Mouth promotion is among the effective methods of marketing and is highly regarded by many commercial companies. This type of marketing is mapped on the influence maximization problem (IMP) in the social networks, and its goal is finding a specific set of the individuals with the maximum influence on the network. Therefore, in this paper, a heuristic-greedy algorithm named the HEDVGreedy algorithm was proposed for the IMP in the social networks. In this algorithm, the expected diffusion value of the graph nodes was calculated using the heuristic method, and then, the effective nodes were selected using the greedy method. Experimental results showed that the proposed algorithm has a high performance than the baseline algorithms while, it significantly reduces the running time of the computations under both the Independent Cascade and Weighted Cascade models in the eight real-world data sets.

关键词： influence spread influence maximization problem Social network Viral marketing

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IMT: Selection of Top-k Nodes based on the Topology Structure in Social Networks 6

IMT: Selection of Top-k Nodes based on the Topology Structur...

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6th International Conference on Web Research (ICWR)

作者： Beni, Hamid Ahmadi Aghaee, Zahra Bouyer, Asgarali Vahidipour, Mehdi Azarbaijan Shahid Madani Univ Tabriz Dept Informat Technol & Commun Tabriz Iran Shahid Rajaee Teacher Training Univ SRTTU Fac Comp Engn Tehran Iran Univ Kashan Fac Elect & Comp Engn Dept Comp Kashan Iran

ISBN: (纸本)9781728110516

influence maximization is a problem based on diffusion and probability in social networks with the aim of finding the least k node with the most influence. These nodes play an essential role in the diffusion process. However, the influence maximization problem faces two essential challenges of time efficiency and optimal selection of the seed nodes. To solve these challenges, we proposed an algorithm based on the properties of the graph topology structure and centrality, called IMT (influence maximization based on the Topology) algorithm. This algorithm selects the seed nodes from the dense part of the graph that can access more nodes in the shortest distance. Finally, experiments showed that the proposed algorithm outperformed the other algorithms in terms of influence spread and running time.

关键词： influence maximization problem Information Diffusion influence Spread Graph Topology Centrality

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Socio-spatial influence maximization in location-based social networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 304-314页

作者： Hosseinpour, Mohammad Malek, Mohammad Reza Claramunt, Christophe KN Toosi Univ Technol Fac Geodesy & Geomat Engn Dept GIS Tehran Iran Naval Acad Res Inst BP 600 F-29240 Brest France

Identifying influential nodes in social networks is a key issue in many domains such as sociology, economy, biology, and marketing. A common objective when studying such networks is to find the minimum number of nodes with the highest influence. One might for example, maximize information diffusion in social networks by selecting some appropriate nodes. This is known as the influence maximization problem (IMP). Considering the social aspect, most of the current works are based on the number, intensity, and frequency of node relations. On the spatial side, the maximization problem is denoted as the Location-Aware influence maximization problem (LAIMP). When advertising for a new product, having access to people who have the highest social status and their neighbors are distributed evenly across a given region is often a key issue to deal with. Another valuable issue is to inform the maximum number of users located around an event, denoted as a query point, as quickly as possible. The research presented in this paper, along with a new measure of centrality that both considers network and spatial properties, extends the influence maximization problem to the location based social networks and denotes it hereafter as the Socio-Spatial influence maximization problem (SSIMP). The focus of this approach is on the neighbor nodes and the concept of line graph as a possible framework to reach and analyze these neighbor nodes. Furthermore, we introduce a series of local and global indexes that take into account both the graph and spatial properties of the nodes in a given network. Moreover, additional semantics are considered in order to represent the distance to a query point as well as the measure of weighted farness. Overall, these indexes act as the components of the feature vectors and using k-nearest neighbors, the closest nodes to the 'ideal' node are determined as top-k nodes. The node with maximum values for feature vectors is considered as the 'ideal' node. The experi

关键词： Location-based social networks Information diffusion Line graph influence maximization problem Feature vectors k-nearest neighbors

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SEIM: Search economics for influence maximization in online social networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 93卷 1055-1064页

作者： Tsai, Chun-Wei Liu, Shih-Jui Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan

The influence of online social networks (OSN), which can be regarded as part of our life, is evident today. As expected, a great deal of useful information about the humans is hidden in the data, such as interpersonal relationship and personal preference. The influence maximization problem (IMP) is one of the well-known problems in this research domain that has attracted the attention of researchers from different disciplines in recent years. One of the reasons is that it can speed up the propagation of information in OSN if we can find out users that have maximum influence on other users. However, traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although many recent studies attempted to use metaheuristic algorithms to solve the IMP, there is still plenty of room for improvement. The proposed algorithm, called search economics for influence maximization (SEIM), is motivated by the concept of return on investment to design its search strategies. As far as the proposed algorithm is concerned, the search strategy of SEIM is like making a good plan to determine how to invest the high potential investment subjects (i.e., regions) in the market (i.e., search space). The experimental results show that the proposed algorithm is significantly better than the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithm Search economics influence maximization problem Online social networks

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CNLPSO-SL: A two-layered method for identifying influential nodes in social networks

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2018年第2期22卷 109-123页

作者： Pourkazemi, Maryam Keyvanpour, Mohammadreza Alzahra Univ Dept Comp Engn Specialized Lab Data Min Tehran Iran Alzahra Univ Dept Comp Engn Tehran Iran

In networks, dynamic phenomena such as opinions, behaviors, and information are propagated through connections between entities. Indeed, one of the main issues about a dynamic process is to find a set of individuals with a high influence on other's decisions which is defined as the "influence maximization" problem, and aims to find a subset of nodes to maximize the total number of adopters at the end of the process. In this paper, by combining the community structure and influence maximization problem, we proposed a two-layered method for identifying influential nodes so that in the first layer an optimization-based method is applied to detect the potential communities. Then, in the second layer, a criterion is used which is a tradeoff between the low-relevant centralities and methods with high complexity. Our method is implemented on real social networks with different scales, and the performance is evaluated by using the total number of infected nodes at the end of the process. The experimental results indicate the superiority of our method in comparison to other considered approaches by considering the efficiency and scalability.

关键词： Social network data mining influence maximization problem influential node diffusion model community structure heuristics

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Maximizing influence in a social network: Improved results using a genetic algorithm

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 478卷 20-30页

作者： Zhang, Kaiqi Du, Haifeng Feldman, Marcus W. Xi An Jiao Tong Univ Sch Management Xian 710049 Shanxi Province Peoples R China Xi An Jiao Tong Univ Ctr Adm & Complex Sci Xian 710049 Shanxi Province Peoples R China Stanford Univ Morrison Inst Populat & Resource Studies Stanford CA 94305 USA

The influence maximization problem focuses on finding a small subset of nodes in a social network that maximizes the spread of influence. While the greedy algorithm and some improvements to it have been applied to solve this problem, the long solution time remains a problem. Stochastic optimization algorithms, such as simulated annealing, are other choices for solving this problem, but they often become trapped in local optima. We propose a genetic algorithm to solve the influence maximization problem. Through multi-population competition, using this algorithm we achieve an optimal result while maintaining diversity of the solution. We tested our method with actual networks, and our genetic algorithm performed slightly worse than the greedy algorithm but better than other algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： influence maximization problem Social network Simulated annealing Genetic algorithm

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Social influence determination on big data streams in an online social network

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第21期76卷 22133-22167页

作者： Kumaran, P. Chitrakala, S. Anna Univ Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

Social networks have become a good place to promote products and also to campaign for causes. Maximizing the spread of information in an online social network at a least cost has attracted the attention of publicist's. In general, influence user ranking methods are derived either by a network's topological features or by user features but not both. Existing influence maximization problem (IMP) operates as a modification of greedy algorithms that cannot scale streaming data. Which are time consuming and cannot handle large networks because it requires heavy Monte-Carlo simulation. This is also an NP hard problem in both linear threshold and independent cascade models. Our proposed work aims to address IMP through a Rank-based sampling approach in the Map-Reduce environment. This novel technique combines user and topological features of the network enabling it to handle real-time streaming data. Our experiment of influenced rank-based sampling approach to influence maximization is compared to the greedy approach with and without sampling that exhibits an accuracy of 82%. Performance analysis in terms of running time is reduced from O(n (3)) to O(k n). Where 'k' is the size of the sample dataset and 'n' is the number of user's.

关键词： Rank-based sampling Information diffusion modelling influence maximization problem Influential spreaders ranking

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Extended methods for influence maximization in dynamic networks

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Computational Social Networks 2018年第1期5卷 8页

作者： Murata, Tsuyoshi Koga, Hokuto Department of Computer Science School of Computing Tokyo Institute of Technology W8-59 2-12-1 Ookayama Meguro Tokyo 152-8552 Japan

Background: The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting nodes. If we can select nodes that contribute to large-scale diffusion, the nodes are expected to be important for viral marketing. Given a network and the size of the starting nodes, the problem of selecting nodes for maximizing information diffusion is called influence maximization problem. Methods: We propose three new approximation methods (Dynamic Degree Discount, Dynamic CI, and Dynamic RIS) for influence maximization problem in dynamic networks. These methods are the extensions of previous methods for static networks to dynamic networks. Results: When compared with the previous methods, MC Greedy and Osawa, our proposed methods were found better than the previous methods: Although the performance of MC greedy was better than the three methods, it was computationally expensive and intractable for large-scale networks. The computational time of our proposed methods was more than 10 times faster than MC greedy, so they can be computed in realistic time even for large-scale dynamic networks. When compared with Osawa, the performances of these three methods were almost the same as Osawa, but they were approximately 7.8 times faster than Osawa. Conclusions: Based on these facts, the proposed methods are suitable for influence maximization in dynamic networks. Finding the strategies of choosing a suitable method for a given dynamic network is practically important. It is a challenging open question and is left for our future work. The problem of adjusting the parameters for Dynamic CI and Dynamic RIS is also left for our future work. © 2018, The Author(s).

关键词： Dynamic networks influence maximization problem SI model

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