Top PDF A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

6.2 Socially Aware Routing 6.2.1 Overview One of the most active topics in the socially aware net- working research field is routing/forwarding schemes in opportunistic networks [ 20 ]. Opportunistic networks, or DTNs, are attracting attention from many researchers as promising platforms for realizing communication without constructing additional infrastructure [ 169 , 176 ]. Many so- cially aware routing schemes for such networks have been proposed [ 20 , 177 – 191 ]. Here, we consider the problem of delivering message M from node s to node t in an oppor- tunistic network. In an opportunistic network, the existence of a path between the source and destination nodes cannot be assumed [ 169 ]. Therefore, many opportunistic network routing schemes adopt the store-carry-and-forward paradigm for message delivery [ 176 ]. In such schemes, when node u with message M meets node v (i.e., node u and v are close to each other), message M can be forwarded to node v. By repeating such message forwarding, a message will be delivered from source node s to destination node t. The simplest way to achieve message delivery in opportunistic networks is by using epidemic routing [ 192 ], in which each node forwards messages to every encountered node. While epidemic routing achieves optimally low message delivery delay, it consumes significant network resources. There- fore, forwarding a message to relay nodes that have a high probability of meeting the destination node is a key issue in the design of routing schemes [ 176 ]. Socially aware rout- ing schemes utilize SNA for estimating the likelihoods of future contacts among nodes, and the estimated likelihoods are used in message forwarding [ 20 ].
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Title: SURVEY OF DATA MINING TECHNIQUES FOR SOCIAL NETWORKING WEBSITES

Title: SURVEY OF DATA MINING TECHNIQUES FOR SOCIAL NETWORKING WEBSITES

Different data mining techniques are used in social network analysis as covered in this work. The techniques are ranging from unsupervised to semi-supervised and supervised learning methods. As far, different levels of improvements have been achieved either with combined or solitary techniques. The result of the experiments conducted on social network analysis is supposed to have shed more light on the structure and activities of social networks. The varied experimental results have also established the bearing of data mining techniques in retrieve valuable information and contents from huge data generated on social network. Future survey will tend to investigate novel up to date data mining techniques for social network analysis. The survey will compare parallel data mining tools and suggest the most fitting tool(s) for the dataset to be analysed. The table also contain the approaches engaged, the experimental results and the dates and authors of the approaches.
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Security Metrics In Social Networking Applications

Security Metrics In Social Networking Applications

social privacy is harder to warranty, does a higher level of concern for internet privacy affect the use of social networking sites? Levitt and Cheung [3] presented the common techniques used in faulttolerance and security. They provided security counterparts to the most common fault-tolerance terms. Meadows [4] presented an outline of a fault model for security and showed how it could be applied to both fault tolerance and fault forecasting in computer security. Jonsson [5][6] proposed an integrated framework for security and dependability from the viewpoint of behavioral and preventive terms. Meadows and Mclean [7] surveyed each part of the taxonomy for fault tolerance and described the research and practices in security that corresponded to it. Avizienis et al. [8] refined the concept of dependability and security by emphasizing on security. In the other hand we should consider Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes (Fig.1). Social network analysis (related to network theory) has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics, and has become a popular topic of speculation and study .Also Several analytic tendencies distinguish social network analysis. There is no assumption that groups are the building blocks of society, the approach is open to studying less bounded social systems, from nonlocal communities to links among websites.
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A Survey on Using Data Mining Techniques for Online Social Network Analysis

A Survey on Using Data Mining Techniques for Online Social Network Analysis

Liben-Nowell and Kleinberg [26] have dealt with link prediction in social networks but which works with only a static snapshot of a network. Hasan et al. [27] have proposed several classification models for link prediction which provides a comparison of several features by showing their rank of importance as obtained by different algorithms. Fouss et al. [28] have presented a link prediction technique based on a Markov-chain model of random walk but which does not scale well for large databases. Zheleva et al. [29] have used a binary classification algorithm in which family relationships were used for link prediction. Tylenda et al. [30] have proposed time-aware and time-agnostic maximum entropy methods for link prediction but have tested data sets only from scientific collaboration networks. Chen et al. [31] have made a detailed study and comparison of four different algorithms for link prediction. Schifanella et al. [32] have proposed a sampling link-prediction algorithm which can help users find friends with similar topical interests.
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A Review Analysis on Anomaly Detection Using Data Mining Techniques in Social Networking

A Review Analysis on Anomaly Detection Using Data Mining Techniques in Social Networking

Abstract:Nowadays, there exists a broad development in utilizing Internet in long range internet in social networking (communication (e.g., texting, video collections, and so forth.), social insurance, online business, bank exchanges, and numerous different administrations. These Internet applications require a palatable level of security and protection. Then again, our computers are under assaults and defenseless against numerous dangers. There is an expanding accessibility of apparatuses and traps for assaulting and intruding networks. Anomalous exercises in social organizations speak to abnormal and unlawful exercises showing distinctive practices than others exhibit in a similar structure. This paper talks about various sorts of abnormalities and their novel order in view of different qualities. A survey of number of procedures for avoiding and distinguishing anomalies alongside fundamentalsuppositions and explanations behind the nearness of such inconsistencies is shrouded in this paper. The paper displays an audit of number of data mining approaches used to recognize anomalies.
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Socially aware integrated centralized infrastructure and opportunistic networking: a powerful content dissemination catalyst

Socially aware integrated centralized infrastructure and opportunistic networking: a powerful content dissemination catalyst

In order to avoid any unexpected collision and interference, a time-division multiple access (TDMA) scheme 4 is implemented in the medium access control (MAC) layer. The related trans- mission frame structure is also portrayed in Fig. 4b, where a single time slot of a transmission frame only allows a single transmitter to forward the CoCI. Classic round-robin scheduling can be applied in the MAC layer due to its low-com- plexity nature. However, for the sake of further improving the attainable performance of the CoCI dissemination, we could carefully select the most suitable CoCI owners for forwarding the CoCI to the hitherto unserved MUs during the next stage of CoCI dissemination. As a result, we should evaluate the potential impact of a CoCI owner on all the unserved MUs with the aid of social network analysis tools [8]. During the second stage of the cooperative multicast aided CoCI dissemination portrayed in Fig. 4, the CI plays the role of a central controller in order to facilitate control signaling exchange and to effi- ciently schedule the transmissions of multiple CoCI multicasters.
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A Survey on Sentiment Analysis on Social Network Data

A Survey on Sentiment Analysis on Social Network Data

Sentiment analysis is an area of research in educational as well as commercial field. The word sentiment denotes the moods or attitude of the person to some particular domain. Therefore it is also known as opinion mining. Opinions of a person may differ from another person. Opinion mining also leads to the particular impersonations on the domain, not facts since the sentiment analysis are mostly topic based. Sentiment classification involves the classification of the polarity and the emotions . Sentiments can be analyzed and classified either by machine learning techniques or by lexicon based techniques. Sentiment analysis allows an user to get a clear idea regarding the “customer satisfaction and dissatisfaction” which For example, “public opinion on new launch of google’s phone” etc. In the commercial world, consumer’s feelings or opinion towards some product or product are very significant for its sell. Therefore in decision making and in real world applications ,sentiment analysis plays a major role. Twitter is considered to be the one of the most populous social networking site where millions of users share their suggestions and opinion about the several fields like politics, products, personalities etc. Many study works are done in the arena of sentiment analysis. But then they are only beneficial in modeling and tracing public opinions. Since the exact reasons behind the sentiment variations are not known and Therefore such variations are not useful in decision making. Sentiment analysis has several applications in various fields like political domain, sociology and real time event detection like Tsunami. Earlier studies were done to model and track public opinions. But then with the advancement in technology, today we can use it for interpreting the reasons of the sentiment change in public attitude, mining and summarizing products reviews, to solve the polarity shift problem by performing dual sentiment analysis. Here we use different algorithms/models like Naïve Bayes (NB) classifier, Support Vector Machine (SVM) algorithm and so on.
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Data Mining Techniques for Online Social Network Analysis

Data Mining Techniques for Online Social Network Analysis

Liben-Nowell and Kleinberg [26] have dealt with link prediction in social networks but which works with only a static snapshot of a network. Hasan et al. [27] have proposed several classification models for link prediction which provides a comparison of several features by showing their rank of importance as obtained by different algorithms. Fouss et al. [28] have presented a link prediction technique based on a Markov-chain model of random walk but which does not scale well for large databases. Zheleva et al. [29] have used a binary classification algorithm in which family relationships were used for link prediction. Tylenda et al. [30] have proposed time-aware and time-agnostic maximum entropy methods for link prediction but have tested data sets only from scientific collaboration networks. Chen et al. [31] have made a detailed study and comparison of four different algorithms for link prediction. Schifanella et al. [32] have proposed a sampling link-prediction algorithm which can help users find friends with similar topical interests. Papadimi-triou et al. [2] presented a paper on fast and accurate link prediction in social networking systems but which considers only friendship network and no other features for link prediction.
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A survey of data mining and social network analysis based anomaly detection techniques

A survey of data mining and social network analysis based anomaly detection techniques

Online social networks being the center of attraction for num- ber of applications are best viewed as a graphical structure with nodes and edges depicting the users and their interaction activities respectively. The nodes and edges in a network can be labeled or not depending upon the network structure being studied. Most of the cases involve considering only the binary and static social links in which mere presence of a link is con- sidered sufficiently good without giving any importance to the actual communication activity of users. But going through the literature, it has been observed that earlier research analyzed the significance of users’ actual interactions also. ‘‘No matter what resources are available within a structure, without com- munication activity those resources will remain dormant, and no benefits will be provided for individuals ” [80] . Taking into consideration actual communication activities and interactions of users, the resulting graph, usually called an activity graph [81,82] are drawn. This activity graph can be categorized as a basic activity graph or a weighted activity graph. A graph con- taining similar kind of edges in every pair of nodes irrespective of strong or weak ties between them is called a basic activity graph but weighted activity graph represents a graph structure in which strength of the activity link is also taken into account. The increasing trend of social networks attracted their mis- use by number of malicious individuals also. Hence, the detec- tion of anomalous activities becomes the need of the hour. Sometimes, it becomes difficult to analyze the social networks because of their large size and complex nature and it becomes necessary to prune the networks to include only the most rel- evant and significant relationships [83] . Usually, the presence of an anomaly is considered as a binary property in which anomaly is either present or not, but in some applications the extent to which anomaly is present is considered by giving degree of being an outlier to each object in the data set. As an example, Breunig et al. [33] referred this degree as Local Out- lier factor (LOF).
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A Survey on Privacy in Social Networking Websites

A Survey on Privacy in Social Networking Websites

Social networking websites have become a potential target for attackers due to the availability of sensitive information as well as its large user base. Therefore, privacy and security issues in online social networks are increasing. Privacy issue is one of the main concerns, since many social network users are not careful about what they expose on their social network space. Many social networking sites try to prevent the exploitations, but many attackers are still able to overcome those security counter measures by using different techniques. Social network users may not be aware of such threats. A survey on different privacy frameworks in social networking websites to prevent from the privacy issues are discussed in this paper.
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A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

The Internet in this age has changed the way of communication between people within society. Communication among people takes place through telephone in the earlier days, but now, various online social network sites are available for enabling the communication by content exchange in the form of links, texts, and multimedia posts to share everything among various people [1]. Social network sites such as Twitter, Facebook, Orkut, and LinkedIn generate tremendous amount of data, as more people share their thoughts, feelings, be online to get updates, and share their day-to-day activities. These data are called the social data and are best represented in nodes and edges. These data are collected from multiple users; sources are dynamic in nature and should be updated continuously. The social data which are collected can be used for research purpose or can be used by the data service provider by giving these data to the various other companies, organizations, or other parties such as advertising partners when users agree to the terms and conditions of that service provider. People’s data contain very sensitive and valuable information; advertising companies can take advantage of these data and target a social network user. Social network analysis is used in various fields such as biology, anthropology, sociology, geography, criminology, and information sciences. Researchers from various different fields use these data to improve security of sensitive information which points to the identity of an individual. It is up to the owner of the data whether he/she wants to publish the entire data online or keep some secret data and publish limited information only. There also exist various owners who are sharing the data to the third party applications for data processing, and privacy breach can also occur due to this [2]. At present, we can describe that social network analysis is a technique for investigating the social structures consisting of vertices and edges. A vertex is also called node which represents peoples, groups, organizations, and knowledge entities. The links which connect these nodes are called edges and can hold information which can be sensitive or non-sensitive data [3]. Sharing of these data online may lead to privacy breach. An individual’s privacy is defined as “the right of the individual to whom he/she is communicating, what he/she is sharing, and under what circumstances” [4]. Breaching of privacy occurs when information is leaked without getting the permission of individuals, company, and organization. Therefore, the privacy preservation of individuals or any
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Social networking for anonymous communication systems: a survey

Social networking for anonymous communication systems: a survey

4. Exploitation of Social Networks for Improv- ing Network Anonymization In this section we provide an overview of recent work on the use of social networking techniques to improve the anonymity degree provided by anonymous communication systems. Recent work focuses mainly in exploiting trust relationships provided by social networks and how they can be used in selecting trusted nodes in order to prevent traffic analysis. There is also interesting work on reputation, which could be useful for rating nodes in a network. From an information theory point of view, it is shown that knowing more information about a given node in the network does not necessarily reduce anonymity. Another important application of social networks is the detection of Sybil attacks.
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A Survey on Social Network Applications

A Survey on Social Network Applications

Facebook is a company and online social networking service founded by Mark zuckerberg on February 4, 2004.Facebook is a social networking amenity. It is an affluent site for examiners involved in the tasks and services of social networks, as it comprises diverse usage patterns and technical capacities that bond online and offline links [5]. In addition, earlier studies have recommended that FB handlers incline to hunt for people with whom they have an offline link rather than cruising for complete aliens to meet [6], and they are typically attentive in what their friends are thinking about. The existing mobile Facebook application provisions most of the original services and tasks provided by the Facebook social website. The mobile version also offers an open platform with APIs, which can be used by third party providers to make applications that enhance more functionality to the unique mobile Facebook application, hence allowing users to adore a richer experience. For example, there are numerous new mobile Facebook applications that deliver location-aware services by permitting users to inform their geographic status, surf the current locations of their Facebook friends, and order the friends by their distances from the users ‘present locations.
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SOCIALLY AWARE COMPUTING SURVEY RESULTS

SOCIALLY AWARE COMPUTING SURVEY RESULTS

The large number of various sensing devices, such as ubiquitous sensors (e.g. RFID, motion sensors, microphones, cameras, etc.), combined with email and Web (e.g. social network sites, blogs and Wiki) offer a lot of data for analyz- ing human behaviour and interaction. Mobile social networking is becoming a new research domain to show the power of merging social networking and mobile computing. It will revolutionize social networking by enabling anytime anywhere social interaction and a higher degree of intelligence. Motivated by the observation that the explosive growth of social networks such as Facebook and Twitter, the popularization of smartphones such as the iPhone, and the rapid evo- lution of sensor networks provide an opportunity to achieve a more comprehen- sive understanding of the context surrounding a user in a given environment [ZYGW14].
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Social Networking Analysis

Social Networking Analysis

Social Networks are social structures made up of nodes and ties; they indicate the rela- tionships between individuals or organisations and how they are connected through social familiarities. They are very useful for visualising patterns. They operate on many levels and play an important role in solving problems, on how organisations are run and they helps individuals succeed in achieving their targets and goals. In today’s society social networks allow two people in different locations to interact with each other socially (e.g. chat, viewable photos, etc.) over a network. They are also important for the Social Safety Net because this is helping the society with the likes of the homeless, unemployment. Group politics relate to ‘In-Groups’ and ‘Out-Groups’ as each competes with each other. The use of mathematical and graph- ical techniques in social network analysis is important to represent the descriptions of networks compactly and more efficiently. Social Networking is all around us and so there is always going to be friends and casual acquaintances both within the sub- groups and outside it. These status types link all sub-groups together as well as the internal structure of a group. Hence there are direct and in-direct connections to link everyone together within social circle websites like facebook.com.
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A Survey on Social Network Analysis for Counter- Terrorism

A Survey on Social Network Analysis for Counter- Terrorism

Social Network Analysis (SNA) is the application of network theory to analyze social networks in terms of social relationships. It comprises of nodes (actors, persons, organizations etc.) within the network and ties representing relationships (friendship, kinship, conversation, financial transaction etc.) among the nodes. Social relationships may be in the form of real world offline social networks (like friendship, kinship, communication, transaction etc.) or it may be online social networks (like Facebook, Twitter etc.). Various SNA measures has been used for representing interaction among actors, examining strong or weak ties, identifying key/central players and subgroups in network, finding topology and strength of network. Recently SNA has appeared as a practice in various domains. It is significantly applied in Information Science, Political Science, Organizational Studies, Social Psychology, Biology, Communication Studies, Business Analysis, Economics and Intelligence Analysis. Facebook, Twitter and few more social networking sites use various measures of SNA to develop strategies and policies for users.
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Applications of Social Network Analysis to Community Dynamics

Applications of Social Network Analysis to Community Dynamics

Another area that has gained a lot of popularity is security. The security assurance of network is critical to the whole modern world. As the increasingly occurred common network security incidents show, current network security approaches are not sufficient. Zhao et al. [16] present a general framework for intelligent analysis and monitoring the security of network information content in high-speed network. The system can intelligently gather and transform various channels of non- structured, semi-structured and structured data based on broadband network, carry on security assurance related characteristic selections and topic identification, perform social network analysis of email. The system can help information security experts find the association rules in the results from various analyzing levels, and visualize association patterns by their relational structures from Link analysis techniques and provide early warning to system administrators. Peer-to-Peer networks can be seen as truly distributed computing systems. Each peer is both a client and a server in these networks. A reasonable trust construction approach for these systems comes from the social network analysis. Zhang et al. [17] proposes a recommendation-based global trust model for Peer-to-Peer network, which is easy to implement. In their model, a peer’s trust information is defined by its past transactions with other peers. Each peer’s global reliability is decided by two factors: one is the reliability of the peer that it transacts with, the other is the corresponding recommendation degree provided by the transaction peer. A peer’s trust value is calculated from the in-degree, corresponding weight (recommendation degree) and the recommend peer’s trust value. They also introduce some security mechanism into this model to defense several attacks, such as tamper, pretend, slander and exaggerate.
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Implementation of Biometric Techniques in Social Networking Sites

Implementation of Biometric Techniques in Social Networking Sites

Keywords: biological characteristics, authentication, encryption, privacy, Social Networking Sites, BioCrytoSystems 1. Introduction Over the past few years, communication through social networking sites has become increasingly popular. Due to their popularity, it’s hard to Figure out the fallacies in the existing system and design a new and improved framework that can produce better results. Members use social networking sites for posts, videos, photographs and many other purposes. Existing security techniques have certain deficiencies due to which their reliability in a widely-networked social media is questionable. In the present scenario, we are facing majority of crimes related to security issues due to leakage of passwords or illegal authentications. This research paper brings the implementation of biometric technology close to social networking sites so that suitableuser authentication can be done [1] in order to save our data from malicious users and other cyber attacks. Biometrics is capable of significantlyreducing security breaches without unjustifiably affecting privacy. Biometric authentication aids the processes of identifying an individual’s identity, authenticating users and non-refutation in information protection.
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A Survey on Social Network Analysis for Counter Terrorism

A Survey on Social Network Analysis for Counter Terrorism

Terrorist Activities worldwide has approached the evolution of various high-ended methodologies for analyzing terrorist groups and networks. Existing research found that Social Network Analysis (SNA) is one of the most effective and predictive method for countering terrorism in social networks. The study reviewed various SNA measures for predicting the key players/ main actors of terrorist network in terms of global as well as Indian perspective. Comparative study among SNA tools demonstrated the applicability and feasibility for online and offline social networks. It is recommended to incorporate temporal analysis using data mining methods. It can enhance the capability of SNA for handing dynamic behavior of online social networks.
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An overview of Software Applications for Social Network Analysis

An overview of Software Applications for Social Network Analysis

E-Net E-Net, a Windows software for analyzing ego network data, was written by Steve Borgatti (2006). The program accepts data pertaining to the egos and to the alters, such as age or sex and it also accepts ties among the alters. E-Net can calculate a number of indices in order to examine the composition of ego networks, the heterogeneity of ego networks, homophily and structural hole measures. In other terms, it calculates how diverse each person’s network is and the tendency of egos to seek similar alters. The numerical analyses are automatically performed on all egos.
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