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2014 | Buch

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Social Networks: A Framework of Computational Intelligence

herausgegeben von: Witold Pedrycz, Shyi-Ming Chen

Verlag: Springer International Publishing

Buchreihe : Studies in Computational Intelligence

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms. The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling. Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social networks, business intelligence and forming collaboration schemes.

Given the subject area, this book is aimed at a broad audience of researchers and practitioners. Owing to the nature of the material being covered and a way it is organized, the volume will appeal to the well-established communities including those active in various disciplines in which social networks, their analysis and optimization are of genuine relevance. Those involved in operations research, management, various branches of engineering, and economics will benefit from the exposure to the subject matter.

Inhaltsverzeichnis

Frontmatter

Detecting Community Structures in Networks Using a Linear-Programming Based Approach: a Review

Abstract

We give an account of an approach to community-structure detection in networks using linear programming: given a finite simple graph G, we assign penalties for manipulating this graph by either deleting or adding edges, and then consider the problem of turning G, by performing these two operations, at minimal total cost into a graph that represents a community structure, i.e., that is a disjoint union of complete subgraphs. We show that this minimization problem can be reformulated (and solved!) in terms of a one-parameter family of linear-programming problems relative to which some kind of a “second-order phase transition” can be observed, and we demonstrate by example that this interpretation provides a viable alternative for dealing with the much studied task of detecting community structures in networks. And by reporting our discussions with a leading ecologist, we demonstrate how our approach can be used to analyse food webs and to support the elucidation of their “global” implications.

William Y. C. Chen, Andreas Dress, Winking Q. Yu

Personalization of Social Networks: Adaptive Semantic Layer Approach

Abstract

This work describes the idea of an adaptive semantic layer for large-scale databases, allowing to effectively handle a large amount of information. This effect is reached by providing an opportunity to search information on the basis of generalized concepts, or in other words, linguistic descriptions. These concepts are formulated by the user in natural language, and modelled by fuzzy sets, defined on the universe of the significances of the characteristics of the data base objects. After adjustment of user’s concepts based on search results, we have “personalized semantics” for all terms which particular person uses for communications with data base or social networks (for example, “young person” will be different for teenager and for old person; “good restaurant” will be different for people with different income, age, etc.).

Alexander Ryjov

Social Network and Formal Concept Analysis

Abstract

In this contribution we present possible using of Formal Concept Analysis, a special method of relational data analysis, in the Social Network research. Firstly, we recall basic information about the classical Ganter and Wille’s version of Formal Concept Analysis, and about our one-sided version of it. Then we give information about our experiment with a social network of students from one school class. Each pupil has characterized his/her relationships to all schoolmates by value from the given range. Then we use one-sided fuzzy Formal Concept Analysis and especially modified Rice-Siff’s algorithm to form clusters. In the end we interpret the results, i.e. interesting groups of students which are viewed by their schoolmates in a similar way, as groups of friends.

Stanislav Krajči

Explaining Variation in State Involvement in Cyber Attacks: A Social Network Approach

Abstract

Cyber attacks pose an increasing threat to the international system. However, not all states are involved in cyber attacks to the same degree. This chapter asks how we might best explain variation in the involvement of states in cyber attacks across the international system. Conceiving the inter-state system as a social network, we hypothesize that as the global interconnectedness of a state increases so will its involvement in cyber attacks. Our regression models provide support for the contention that the more connected a state is to other states in the international system, the more likely it is to be involved in cyber attacks.

Eric N. Fischer, Ciprianna M. Dudding, Tyler J. Engel, Matthew A. Reynolds, Mark J. Wierman, John N. Mordeson, Terry D. Clark

Moblog-Based Social Networks

Abstract

Social Network (SN) represents the relationship among the social entities, like friends, co-workers, co-authors. Online Social Network (OSN) sites attracted the people who are scattered all over the world. It is an application of web 2.0, which facilitates the users to interact among themselves, nevertheless of considering the geographical locations. Hence, these sites are having unprecedented growth. The members of these sites can establish networking by viewing the profiles of similar-interested persons. A blog is a content posted over a website or a web page, usually arranged in reverse chronological order. Blogs which are hosted by using specialized mobile devices like iPad, Personal Digital Assistants (PDA), are called mobile blogs (or shortly called moblog). A moblog facilitates habitual bloggers to post write-ups directly from their phones even when “on-the-move”. There are different ways to establish the communication among the users of social networking sites and to form the social networking environment among them. One such way is through blog posts hosted on a website. The habitual users who responded to a blog post can be connected by links between them and this structure will grow as a network. Hence, the users (responders) will form the graph structure. An interesting research phenomenon from such an environment would be to extract a subgraph of users based on some common property, and such structure can be called as communities. Discovering communities by partitioning a graph into subgraph is an NP-hard problem. Hence, a machine learning method, clustering, is applied to discover communities from the graph of social network which is formed from the mobile bloggers.

A. Abdul Rasheed, M. Mohamed Sathik

Uncertainty-Preserving Trust Prediction in Social Networks

Abstract

The trust metric has became an increasingly important element of social networks. While collecting, processing and sharing information is becoming easier and easier, the problem of the quality and reliability of that information remains a significant one. The existence of a trust network and methods of predicting trust between users who do not know each other are intended to help in forming opinions about how much to trust information from distant sources. In this chapter we discuss some basic concepts like trust modeling, trust propagation and trust aggregation. We briefly recall recent developments in the area and present a new approach that focuses on the uncertainty aspect of trust value. To this end we utilize a theory of incompletely known fuzzy sets and we introduce a new, uncertainty-preserving trust prediction operator based on group opinion and on relative scalar cardinality of incompletely known fuzzy sets. Motivated by the need for proper uncertainty processing, we have constructed a new method of calculating relative scalar cardinality of incompletely known fuzzy sets that ensures the monotonicity of uncertainty. We outline the problem of uncertainty propagation, and we illustrate by examples that the proposed operator provides most of the desirable properties of trust and uncertainty propagation and aggregation.

Anna Stachowiak

Impact of Social Network Structure on Social Welfare and Inequality

Abstract

In this chapter, how the structure of a network can affect the social welfare and inequality (measured by the Gini coefficient) are investigated based on a graphical game model which is referred to as the Networked Resource Game (NRG). For the network structure, the Erdos–Renyi model, the preferential attachment model, and several other network structure models are implemented and compared to study how these models can effect the game dynamics. We also propose an algorithm for finding the bilateral coalition-proof equilibria because Nash equilibria do not lead to reasonable outcomes in this case. In economics, increasing inequalities and poverty can be sometimes interpreted as a circular cumulative causations, such positive feedback is also considered by us and a modified version of the NRG by considering the positive feedback (p-NRG) is proposed. The influence of network structures in this new model is also discussed at the end of this chapter.

Zhuoshu Li, Zengchang Qin

Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Abstract

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure–function relationship. Therefore, detecting communities (or modules) can be a way to identify substructures that could correspond to important functions. Community detection can be viewed as an optimization problem in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the detection problem. However, those approaches have drawbacks because they attempt to optimize only one objective function, this results in a solution with a particular community structure property. More recently, researchers have viewed the community detection problem as a multi-objective optimization problem, and many approaches have been proposed. Genetic Algorithms (GA) have been used as an effective optimization technique to solve both single- and multi-objective community detection problems. However, the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years. We show how those objectives correlate, investigate their performance when they are used in both the single- and multi-objective GA, and determine the community structure properties they tend to produce.

Ahmed Ibrahem Hafez, Eiman Tamah Al-Shammari, Aboul ella Hassanien, Aly A. Fahmy

Computational Framework for Generating Visual Summaries of Topical Clusters in Twitter Streams

Abstract

As a huge amount of tweets become available online, it has become an opportunity and a challenge to extract useful information from tweets for various purposes. This chapter proposes a novel way to extract topical structure from a large set of tweets and generate a usable summarization along with related topical keywords. Our system covers the full span of the topical analytics of tweets starting with collecting the tweets, processing and preparing them for text analysis, forming clusters of relevant words, and generating visual summaries of most relevant keywords along with their topical context. We evaluate our system by conducting a user study and the results suggest that users are able to detect relevant information and infer relationships between keywords better with our summarization method than they do with the commonly used word cloud visualizations.

Miray Kas, Bongwon Suh

Granularity of Personal Intelligence in Social Networks

Abstract

This chapter introduces and exposes the latest development of agent-modeling framework that emerges from understanding the human behaviour of personal knowledge management (PKM) in social networks. The growing interest in personal knowledge management in exposing personal intelligence has brought out the postulation of GUSC model, which becomes the basis of agent-mediated PKM. In micro view, this GUSC model is the significant tool in understanding the granularity of personal intelligence in social networks. It eventually leads to a theoretical treatment on the bottom-up manifestation of organisational knowledge management from individual PKM processes.

Shahrinaz Ismail, Mohd Sharifuddin Ahmad, Zainuddin Hassan

Social Network Dynamics: An Attention Economics Perspective

Abstract

Within social networking services, users construct their personal social networks by creating asymmetric or symmetric social links. They usually follow friends and selected famous entities, such as celebrities and news agencies. On such platforms, attention is used as currency to consume the information. In this chapter, we investigate how users follow famous entities. We analyze the static and dynamical data within a large social networking service with a manually classified set of famous entities. The results show that the in-degree of famous entities does not fit to a power-law distribution. Conversely, the maximum number of famous followees in one category for each user shows a power-law property. Finally, in an attention economics perspective, we discuss the reasons underlying these phenomena. These findings might be helpful in microblogging marketing and user classification.

Sheng Yu, Subhash Kak

A Framework to Investigate the Relationship Between Employee Embeddedness in Enterprise Social Networks and Knowledge Transfer

Abstract

Organizations introduce Enterprise Social Networks to support knowledge management and in particular to facilitate knowledge transfer. However, to reap the full benefit of Enterprise Social Networks it is necessary to understand the relations and the interactions between employees within these networks. This book chapter provides a literature-based theoretical framework that enables the analysis of the relationships between an employee’s embeddedness in an Enterprise Social Network, their access to social capital, their individual knowledge transfer process and the achieved knowledge transfer in an organization. We develop network-based measures that can be extracted for each framework element using data mining techniques and discuss the relationships among the framework elements. Additionally, suggestions on how to process the network measures using Computational Intelligence methods, e.g., fuzzy logic, are presented. Establishing a strong theoretical groundwork, this book chapter encourages future research crossing the boundaries between information systems, Computational Intelligence, organizational science, and knowledge management.

Janine Viol, Carolin Durst

A Novel Approach on Behavior of Sleepy Lizards Based on K-Nearest Neighbor Algorithm

Abstract

The K-Nearest Neighbor algorithm is one of the commonly used methods for classification in machine learning and computational intelligence. A new research method and its improvement for the sleepy lizards based on the K-Nearest Neighbor algorithm and the traditional social network algorithms are proposed in this chapter. The famous paired living habit of sleepy lizards is verified based on our proposed algorithm. In addition, some common population characteristics of the lizards are also introduced by using the traditional social net work algorithms. Good performance of the experimental results shows efficiency of the new research method.

Lin-Lin Tang, Jeng-Shyang Pan, XiaoLv Guo, Shu-Chuan Chu, John F. Roddick

An Evaluation Fuzzy Model and Its Application for Knowledge-Based Social Network

Abstract

Knowledge-based organizations (KBOs) such as universities, research institutes, and research centers at businesses and industries manage their projects in pursuit of the goals of their organizations. It is well understood that collaboration becomes one of the most important factors for successful completion of these projects. In performing a project jointly, it is important for project team members to know who has the required knowledge. Thus it is imperative to assess what and how much the employees know. Using a knowledge-based social network and its basic approach, a new method is proposed to analyze the knowledge and collaboration collectively possessed by the employees of a KBO. This study first deal with the methodologies of evaluating knowledge and collaboration possessed both by individuals and the organization. Since the quantitative evaluation is essential in developing a series of evaluating methods, the measures of knowledge and collaboration are derived. Then, the knowledge network types of KBO and the network roles of KBO members are discussed. Four types of knowledge-based social network and four roles of network members are also discussed respectively. An evaluation fuzzy model is proposed to test the feasibility of the knowledge-based social network and its measures. A case study is used to demonstrate effectiveness of the proposed model.

Hyeongon Wi, Mooyoung Jung

Process Drama Based Information Management for Assessment and Classification in Learning

Abstract

In this chapter we present a formal description of information management for assessment and classification in learning. The description is supported by a structure related to drama process for learning. Our logic follows the idea of invoking uncertainties using underlying categories, and the language of processes in ‘drama process’ is taken to be BPMN (Business Process Modelling Notation).

Heidi-Tuulia Eklund, Patrik Eklund

Social Network Computation for Life Cycle Analysis by Intelligence Perspective

Abstract

In this study, a method is proposed to calculate the product life cycle by fuzzy intelligent scheme. The product life cycle is estimated by three factors: carbon footprint, environmental management system, and environmental performance evaluation. The social network and mechanism of product life cycle assessment can be considered from the three perspectives. (1) Benefits, including organizational interests, individual interests; (2) Cost: including time, financial resources, and negative influence; (3) Difficulty in execution, including the ability of the employees, the support from a company’s senior management. Each question can be answered on a five-point scale or directly from subjective judgment. The operational definition of various assessment criteria is also considered. There are nine different strategies proposed within the three factors. The final verification of these strategies was made by comparing their comprehensive evaluation factors. In practice, the approach is relatively straightforward. It also takes into account the product life cycle related to social networks and the impact of subjective and objective factors.

Wang-Kun Chen, Ping Wang

Analyzing Tweet Cluster Using Standard Fuzzy C Means Clustering

Abstract

Since the inception of Web 2.0. the effort of socializing, interacting and referencing has been substantially enhanced.This is completely aided through the various means of social network expansions like blogging, public chat rooms and social networking sites such as Facebook, Twitter etc. Behavior on these websites leaves an electronic trail of social activity which can be analyzed and valuable information can be discerned. The development of such analysis has become phenomenal to foster psychological analysis, behavioral modeling and even commercializing the business activities under those paradigms itself. Therefore, micro-blogging service Tweeter recently has gained much interest to social network community with the trend of its Follower/Following Relationship, Mentions, trends, retweet, Twitter Lists etc. and the result of such impact could be realized while investigating diversified tweet clusters under the same community and under the same relevant discussion of topic. This chapter initiates a novel idea to analyze the random tweet cluster and its relevant trend through computational intelligence e.g. through Standard Fuzzy C Means clustering. The idea solicits and introduces a better method of clustering with more number of actually found dynamic clusters. Results have been evaluated with broader implication of analysis and research in futuristic Tweeter network.

Soumya Banerjee, Youakim Badr, Eiman Tamah Al-Shammari

The Global Spread of Islamism: An Agent-Based Computer Model

Abstract

We use an agent-based model to model a dynamic network that considers the rate at which Islamism will spread globally. We define Islamism as the organized political trend that seeks to solve modern political problems by referencing Muslim teachings. The trend is also associated with Radical Islam or Islamic Fundamentalism and is often revolutionary and violent in nature. The model assumes that Islamism spreads from state to state based on existing relations that replicate those defining global trade and communications. Islamism must diffuse through these existing networks. Since Islamism is inimical to western liberal values such as women’s rights and social tolerance, the diffusion of Islamism is hindered by a strong commitment to western liberal values. We include all countries in the analysis, scored on the degree to which they are committed to Islamism and western liberal values.

Morgan L. Eichman, James A. Rolfsen, Mark J. Wierman, John N. Mordeson, Terry D. Clark

Using Neural Network Model to Evaluate Impact of Economic Growth Rate and National Income Indices on Crude Birth Rate in Taiwan

Abstract

In this study, a conceptual social network, in which the artificial neural network (ANN) was used, was adopted to evaluate impact of economic growth rate (EGR) and national income indices (NII) on crude birth rate (CBR) in Taiwan. The NII included gross domestic product (GDP), GDP per capita (GDPPC), gross national product (GNP), GNP per capita (GNPPC), national income (NI), and NI per capita (NIPC). To establish the ANN model, the EGR and NII were taken as the input variables, and the CBR was taken as the output variable. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 23.29 %, 46.02, 6.78, and 0.85, respectively when training. Those for testing were 28.93 %, 35.82, 5.99, and 0.70, respectively. The results showed that the CBR appeared to have a negative sensitivity towards three per capita indices including GDPPC (−0.0369), GNPPC (−0.1314), and NIPC (−0.3822). It suggested that the “capital dilution” would result in CBR decline. But positive EGR in a previous year should stimulate the CBR in the current year, as well as the positive macroeconomic factors including GDP, GNP, and NI. It suggested that the economic development would cause the fertility will to occur, thus increased the CBR.

Yi-Ti Tung, Tzu-Yi Pai

Backmatter

Titel: Social Networks: A Framework of Computational Intelligence
herausgegeben von: Witold Pedrycz
Shyi-Ming Chen
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-02993-1
Print ISBN: 978-3-319-02992-4
DOI: https://doi.org/10.1007/978-3-319-02993-1