Influential node detection in social network during community detection
03 Mar 2015-pp 1-6
TL;DR: This paper is an effort to understand and model the complexity of the dynamics of different metrics of social network.
Abstract: The proliferation of social media has inundated a gamut of research purviews e.g. Node classification, Community analysis, Behavioral psychological in social media. Out of these research issues the study of role of individual in terms of influential phrase plays a crucial role in deciding the future and popularity of any online social community. With the deep analysis of the community, the various parameters like: density, clustering coefficient, degree centrality, closeness centrality, eigenvector centrality etc. can be extracted with an effective and reliable way. These features can be easily exploited to determine the most influential individual /node in an online community. Motivated by the complexity of research problem of finding most influential nodes in an online evolving community, this paper is an effort to understand and model the complexity of the dynamics of different metrics of social network. The proposed scheme is corroborated with the rigorous result analysis.
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01 Jan 2016
TL;DR: The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing, providing the readers with a deep understanding of the state of the art solutions.
Abstract: The rapid growth of the World Wide Web and social media allows users playing an active role in the contents’ creation process. Users can evaluate the brands’ reputation and quality exploiting the information provided by new marketing channels, such as social media, social networks , and electronic commerce (or e-commerce). Consequently, enterprises need to spot and analyze these digital data in order to improve their reputation among the consumers. The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing , providing the readers with a deep understanding of the state of the art solutions.
22 citations
TL;DR: This work analyzes Raktakarabi and Muktodhara, two renowned Bengali dramas of Rabindranath Tagore, and proposes an edge contribution-based centrality and diversity metric of a node to determine the influence of one character over others.
Abstract: Literature network analysis is an emerging area in the computational research domain. Literature network is a type of social network with various distinct features. The analysis explores significance of human behavior and complex social relationships. The story consists of some characters and creates an interconnected social system. Each character of the literature represents a node and the edge between any two nodes offered the interaction between them. An annotation and a novel character categorization method are developed to extract interactive social network from the Bengali drama. We analyze Raktakarabi and Muktodhara , two renowned Bengali dramas of Rabindranath Tagore. Weighted degree, closeness, and betweenness centrality analyze the correlation among the characters. We propose an edge contribution-based centrality and diversity metric of a node to determine the influence of one character over others. High diverse nodes show low clustering coefficient and vice versa. We propose a novel idea to analyze the characteristics of protagonist and antagonist from the influential nodes based on the complex graph. We also present a game theory-based community detection method that clusters the actors with a high degree of relationship. Evaluation on real-world networks demonstrates the superiority of the proposed method over the other existing algorithms. Interrelationship of the actors within the drama is also shown from the detected communities, as underlying theme of the narrations is identical. The analytical results show that our method efficiently finds the protagonist and antagonist from the literature network. The method is unique, and the analytical results are more accurate and unbiased than the human perspective. Our approach establishes similar results compared with the benchmark analysis available in Tagore’s Bengali literature.
12 citations
03 Feb 2009
TL;DR: In this paper, the authors study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data and find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks.
Abstract: Background We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. Methodology/Principal Findings We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Conclusion Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.
9 citations
01 Nov 2019
TL;DR: This paper aims to provide a survey on the influence maximization problem and focuses on two aspects, influence diffusion models and proposed approaches for influential nodes detection.
Abstract: Social networks have attracted a great deal of attention and have in fact important information vectors that have changed the way we produce, consume and diffuse information. Social networks' analysis has been of great interest and has encompassed different research areas including community detection, the discovery of web services from social networks, information diffusion, detection of infuential nodes. The process of detecting influential nodes in social networks is often khown as Influence Maximization (IM) problem, it deals with finding a small subset of nodes that spread maximum influence in the network. It has been proved that it has many applications such as the propagation of opinions, the study of the acceptance of political blogs or the study of the degree of adhesion of an actor to a product in marketing (web marketing). A such maximization requieres the presence of a diffusion model that controls information propagation within active individuals. This paper aims to provide a survey on the influence maximization problem and focuses on two aspects, influence diffusion models and proposed approaches for influential nodes detection. We start by describing formally the IM problem, then we will provide the state-of-the-art of both diffusion models and influence maximization algorithms.
8 citations
Cites background from "Influential node detection in socia..."
...It can be shown by the sum of shortest paths between that node and all others in the network [15]....
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...Indeed, in the area of influence maximization, individuals with high scores of centrality are more fortunate to be adopters of information that circulates in the network [15]....
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...• we can also talk about the BC measure (Betweeness Centrality) which gives the number of shortest paths between two individuals in the network [15]....
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13 Jul 2016
TL;DR: This paper proposes a data cleansing process for CDR in order to filter the anomaly numbers and shows that using the proposed solution and ranking metrics could detect influencers and communities accurately.
Abstract: Nowadays, Telecommunication service providers produce a huge volume of calling data records (CDR) each day. A clear understanding of their customers is a key success of any company. To analyze the behaviors and relationships between customers, social network analysis (SNA) is usually employed to detect influencers and communities along with calling behaviors (profiles). Unfortunately, the graph of CDR is different from that of other social media, e.g., Twitter, Facebook, etc. So, the results should be mistaken and cannot reflect the real customers if SNA is directly applied to CDR, such as, misinterpret “telesales” as “influencer”. In this paper, we propose a data cleansing process for CDR in order to filter the anomaly numbers. This can improve the accuracy of the analysis and remove any misinterpreted outcomes. Moreover, a measure is invented to capture influencers based on calling behaviors. The experiment was conducted on 2.5 million calling records of a telecommunication in Thailand. The result showed that using our proposed solution and ranking metrics could detect influencers and communities accurately.
6 citations
Cites background from "Influential node detection in socia..."
...Discovering communities, identifying influencers or identifying actors are all kinds of analysis that can be done for social networks [7, 8, 9, 10]....
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References
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TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
9,441 citations
24 Aug 2003
TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.
5,887 citations
24 Oct 2007
TL;DR: This paper examines data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut, and reports that the indegree of user nodes tends to match the outdegree; the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree node at the fringes of the network.
Abstract: Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale.Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.
3,266 citations
01 May 2000
TL;DR: A method of improving certain characteristics of cadmium mercury telluride single crystal material by heat treating thesingle crystal material in the presence of both tellurium and mercury.
Abstract: Long a matter of folklore, the ``small-world phenomenon'''' --the principle that we are all linked by short chains of acquaintances --was inaugurated as an area of experimental study in the social sciences through the pioneering work of Stanley Milgram in the 1960''s. This work was among the first to make the phenomenon quantitative, allowing people to speak of the ``six degrees of separation'''' between any two people in the United States. Since then, a number of network models have been proposed as frameworks in which to study the problem analytically. One of the most refined of these models was formulated in recent work of Watts and Strogatz; their framework provided compelling evidence that the small-world phenomenon is pervasive in a range of networks arising in nature and technology, and a fundamental ingredient in the evolution of the World Wide Web. But existing models are insufficient to explain the striking algorithmic component of Milgram''s original findings: that individuals using local information are collectively very effective at actually constructing short paths between two points in a social network. Although recently proposed network models are rich in short paths, we prove that no decentralized algorithm, operating with local information only, can construct short paths in these networks with non-negligible probability. We then define an infinite family of network models that naturally generalizes the Watts-Strogatz model, and show that for one of these models, there is a decentralized algorithm capable of finding short paths with high probability. More generally, we provide a strong characterization of this family of network models, showing that there is in fact a unique model within the family for which decentralized algorithms are effective.
2,198 citations
TL;DR: In this paper, the authors investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities and found that individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network.
Abstract: How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.
2,114 citations