Predictive Analytics of Social Networks

2014 ◽  
pp. 297-333
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

Predictive Analytics of Social Networks

2018 ◽  
pp. 823-862
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

Predictive Analytics of Social Networks

2016 ◽  
pp. 1080-1116
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

USING SOCIAL NETWORK PARADIGM FOR DEVELOPING A CONCEPTUAL FRAMEWORK IN CRM

2011 ◽  
Vol 01 (04) ◽  
pp. 63-71
Author(s):  
Mohammad Javad Mosadegh ◽  
Mehdi Behboudi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
New Media ◽  
Conceptual Framework ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Its Analysis ◽  
Analysis Tools

This study develops a conceptual framework for applying social networks in usual CRM models. Recent changing in customer relationship theme and putting new media and network-based paradigm into practice makes it imperative to find how social networks affect CRMs. Accordingly, this study explains the role of social networks in customer relationship management by using its analysis, tools and aspects of this concepts based on CRM models. We have provided a SCRM framework that is based on usual CRM models and incorporates Social networks and its tools, methods and analysis. The framework is combination of Social networks concept and traditional CRM concepts.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Cluster Based Architecture and Algorithm to Improve the Design of Social Networks

2017 ◽  
Vol 9 (3) ◽  
pp. 29-43
Author(s):  
Saurab Dutta ◽  
Payel Roy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Low Cost ◽  
Customer Relationship ◽  
Management Tool ◽  
Goods And Services ◽  
Increasing Demand ◽  
Business Purpose

In a social network people are connected by relationships, business purpose or transaction activity. The increasing demand of social network analysis and how to improve the architecture is of utmost importance for the organizations who are regularly trying to improve the service through social network analysis. Social network analysis views social relationship in terms of network theory. Social networks connect people at very low cost and this network acts as a customer relationship management tool for increasing sales of organization in terms of goods and services. Different models are proposed and utilized in different platforms. In this model, the authors have proposed a cluster-based structure to improve performance of social networks.

scholarly journals An Exploration of Community Detection and Recommendation Systems (CDR) in Social Networks

2021 ◽  
Vol 10 (10) ◽  
pp. 23-33
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Recommender Systems ◽  
Community Detection ◽  
Graphical Models ◽  
Social Relations ◽  
Local Area ◽  
Network Information ◽  
Detection Techniques

Community detection and Recommender systems are assumed as significant parts in helping the web users discover important information by proposing information of likely interest to them and a central task for network analysis means to segment a network into numerous substructures to assist with uncovering their inactive capacities. Community detection has been widely concentrated in and extensively applied to numerous real world network problems. Because of the possible worth of social relations in recommender systems, social recommendation has drawn in expanding consideration in recent years. As the issues that network strategies attempt to solve and the network information to be determined become progressively more complex, new methodologies have been proposed and created, traditional ways to deal with community detection and recommendation commonly use probabilistic graphical models and implement an assortment of earlier information to deduce community structures. Regardless of all the new progression, there is as yet an absence of astute comprehension of the hypothetical and methodological supporting of local area location, which will be fundamentally significant for future advancement of the space of social network analysis. In this paper, we start by giving conventional meanings of social networks terms and talk about the novel property of social networks and its implications. Unified architecture of network community finding methods to characterize the state-of-the-art of the field of community detection. In particular, we give a complete survey of the current community detection techniques and audit of existing recommender systems examine some exploration bearings to further develop social network capabilities.

scholarly journals Spontaneous Neural Encoding of Social Network Position

2017 ◽  
Author(s):  
Carolyn M. Parkinson ◽  
Adam M. Kleinbaum ◽  
Thalia Wheatley
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Human Brain ◽  
Brain Evolution ◽  
Neural Encoding ◽  
Form Complex ◽  
Network Information ◽  
Human Brain Evolution ◽  
The Social ◽  
Social Network Position

Humans form complex social networks that include numerous non-reproductive bonds with non-kin. Navigating these networks presents a considerable cognitive challenge thought to have comprised a driving force in human brain evolution. Yet, little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. By combining social network analysis and multi-voxel pattern analysis of functional magnetic resonance imaging (fMRI) data, we show that social network information about direct relationships, bonds between third parties, and aspects of the broader network topology is accurately perceived and automatically activated upon seeing a familiar other.

scholarly journals A Matrix Factorization Algorithm for Efficient Recommendations in Social Rating Networks Using Constrained Optimization

2019 ◽  
Vol 1 (3) ◽  
pp. 928-944 ◽  
Author(s):  
Nicholas Ampazis ◽  
Theodoros Emmanouilidis ◽  
Flora Sakketou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Constrained Optimization ◽  
Matrix Factorization ◽  
Real Life ◽  
Optimization Techniques ◽  
Information Discovery ◽  
Network Information ◽  
Factorization Algorithm ◽  
And Performance

In recent years the emergence of social media has become more prominent than ever. Social networking has become the de facto tool used by people all around the world for information discovery. Consequently, the importance of recommendations in a social network setting has urgently emerged, but unfortunately, many methods that have been proposed in order to provide recommendations in social networks cannot produce scalable solutions, and in many cases are complex and difficult to replicate unless the source code of their implementation has been made publicly available. However, as the user base of social networks continues to grow, the demand for developing more efficient social network-based recommendation approaches will continue to grow as well. In this paper, following proven optimization techniques from the domain of machine learning with constrained optimization, and modifying them accordingly in order to take into account the social network information, we propose a matrix factorization algorithm that improves on previously proposed related approaches in terms of convergence speed, recommendation accuracy and performance on cold start users. The proposed algorithm can be implemented easily, and thus used more frequently in social recommendation setups. Our claims are validated by experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset crawled from Flixster.com.

scholarly journals General practice and patients’ views of the social networks of patients with multimorbidity

2018 ◽  
Vol 10 (3) ◽  
pp. 258 ◽  
Author(s):  
Eileen McKinlay ◽  
Jessica Young ◽  
Ben Gray
Keyword(s):  
Social Networks ◽  
General Practice ◽  
Social Network ◽  
Clinical Practice ◽  
Active Role ◽  
Practice Staff ◽  
Network Information ◽  
The Social ◽  
Practice Approach ◽  
Practice Knowledge

ABSTRACT INTRODUCTION For patients with multimorbidity to live well, they need the support of not only health professionals but family, friends and organisations. These social networks provide support, potentially enabling the formation of a Community of Clinical Practice approach to multimorbidity care. AIM This study aimed to explore general practice knowledge of the social networks of patients with multimorbidity. METHODS Social network maps were completed by both patients and general practice. The social network maps of 22 patients with multimorbidity were compared with corresponding social network maps completed by general practice staff. RESULTS In 60% (13/22) of the patients, general practice staff held a high or moderate knowledge of individual patients’ social networks. Information on social networks was recalled from staff memory and not systematically recorded in patients’ electronic health records. DISCUSSION Social network information is not routinely collected, recorded or used by general practice to understand the support available to patients with multimorbidity. General practice could take an active role in coordinating social network supporters for certain patient groups with complex multimorbidity. For these groups, there is value in systematically recording and regularly updating their social network information for general practice to use as part of a coordinated Community of Clinical Practice.

Set Pair Social Network Analysis Model

2011 ◽  
Vol 50-51 ◽  
pp. 63-67 ◽  
Author(s):  
Hong Mei Yang ◽  
Chun Ying Zhang ◽  
Rui Tao Liang ◽  
Fang Tian
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dynamic Problems ◽  
Analysis Model ◽  
Network Information ◽  
The Social ◽  
The Relationship

Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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