Need of Intelligent Search in Dynamic Social Network

Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

The user of dynamic social network does not require irrelevant and vast amount of information during a search. A need of an intelligent search is required to get the reduced, filtered and relevant information that is achieved using an intelligent information retrieval and web mining. In this paper, identification and description of facts related to needs of an intelligent search in dynamic social network has been done by the authors after the deep and thorough study conducted on several journal and conference papers that are scattered on different electronic databases globally. The usage of intelligent agent for effective information retrieval from the social network site is a very emerging area and it will help the users to find the relevant and concerned information quickly and efficiently. The findings of the authors will help researchers and scholars who are already working in this area to get the relevant information in the direction of future research.

2018 ◽  
pp. 2338-2351
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

The user of dynamic social network does not require irrelevant and vast amount of information during a search. A need of an intelligent search is required to get the reduced, filtered and relevant information that is achieved using an intelligent information retrieval and web mining. In this paper, identification and description of facts related to needs of an intelligent search in dynamic social network has been done by the authors after the deep and thorough study conducted on several journal and conference papers that are scattered on different electronic databases globally. The usage of intelligent agent for effective information retrieval from the social network site is a very emerging area and it will help the users to find the relevant and concerned information quickly and efficiently. The findings of the authors will help researchers and scholars who are already working in this area to get the relevant information in the direction of future research.


2015 ◽  
Vol 28 (2) ◽  
pp. 46-61 ◽  
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

The user of dynamic social network does not require irrelevant and vast amount of information during a search. A need of an intelligent search is required to get the reduced, filtered and relevant information that is achieved using an intelligent information retrieval and web mining. In this paper, identification and description of facts related to needs of an intelligent search in dynamic social network has been done by the authors after the deep and thorough study conducted on several journal and conference papers that are scattered on different electronic databases globally. The usage of intelligent agent for effective information retrieval from the social network site is a very emerging area and it will help the users to find the relevant and concerned information quickly and efficiently. The findings of the authors will help researchers and scholars who are already working in this area to get the relevant information in the direction of future research.


Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.


2016 ◽  
pp. 458-475
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.


2019 ◽  
Vol Volume 27 - 2017 - Special... ◽  
Author(s):  
Abir Gorrab ◽  
Ferihane Kboubi ◽  
Henda Ghézala

The explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model. L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social.


Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.


2021 ◽  
Author(s):  
◽  
Daniel Donoghue

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>


2021 ◽  
Author(s):  
Daniel Donoghue

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>


2019 ◽  
Vol 2 (1) ◽  
pp. 99-122 ◽  
Author(s):  
Katherine Faust ◽  
George E. Tita

Over the past decade, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of social network analysis by a cadre of scholars. We review recent contributions to four areas of crime research: co-offending networks, illicit networks, gang-rivalry networks, and neighborhoods and crime. Our review highlights potential pitfalls that one might encounter when using social networks in criminological research and points to fruitful directions for further research. In particular, we recommend paying special attention to the clear specifications of what ties in the network are assumed to be doing, potential measurement weaknesses that can arise when using police or investigative data to construct a network, and understanding dynamic social network processes related to criminological outcomes. We envision a bright future in which the social network perspective will be more fully integrated into criminological theories, analyses, and applications.


2011 ◽  
pp. 149-175 ◽  
Author(s):  
Yutaka Matsuo ◽  
Junichiro Mori ◽  
Mitsuru Ishizuka

This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. In this chapter, we specifically examine the social network extraction from the Web using a search engine. The Web is a huge source of information about relations among persons. Therefore, we can build a social network by merging the information distributed on the Web. The growth of information on the Web, in addition to the development of a search engine, opens new possibilities to process the vast amounts of relevant information and mine important structures and knowledge.


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