Networks of Community Formation

2020 ◽  
pp. 129-163
Author(s):  
Jyoti Gulati Balachandran
Keyword(s):  
Social Networks ◽  
Seventeenth Century ◽  
Community Formation ◽  
Entire Region ◽  
Textual Organization ◽  
Joint Enterprise ◽  
Long Chains

By the time of Mughal presence in Gujarat, the textual inscription of the personal and social networks of Sufis and other learned men in texts like Miṣbāḥ al-‘Ālam and Ṣaḥā’if al-sādāt intersected with an identity that was clearly regional and specific to Gujarat. In the seventeenth century, these networks were organized in texts in overlapping and contrasting ways: long chains of spiritual initiation and practice (silsilahs); distinct familial genealogies (silsilat al-naṣab), families (khānwādas), and tribes (qabīlas). This chapter demonstrates that the textual organization of Suhrawardi networks, in particular, was influential in communicating that the joint enterprise of state, community and region formation had been pre-determined: the Suhrawardi Sufis were pre-ordained to inspire and sanctify the entire region of Gujarat with the message of Islam and Sufism. Such historiographical interventions, representing the second narrative moment, were in turn a testimony to the successful expansion of the Suhrawardi lineage in Gujarat.

Rembrandt, Vermeer, and the Gift in Seventeenth-Century Dutch Art

2021 ◽  
Author(s):  
Michael Zell
Keyword(s):  
Social Networks ◽  
Seventeenth Century ◽  
Early Modern ◽  
Gift Exchange ◽  
Gift Giving ◽  
Dutch Art ◽  
Dutch Golden Age ◽  
Symbolic Economy ◽  
The Gift ◽  
New Perspective

This book offers a new perspective on the art of the Dutch Golden Age by exploring the interaction between the gift's symbolic economy of reciprocity and obligation and the artistic culture of early modern Holland. Gifts of art were pervasive in seventeenth-century Europe and many Dutch artists, like their counterparts elsewhere, embraced gift giving to cultivate relations with patrons, art lovers, and other members of their social networks. Rembrandt also created distinctive works to function within a context of gift exchange, and both Rembrandt and Vermeer engaged the ethics of the gift to identify their creative labor as motivated by what contemporaries called a love of art

scholarly journals Visualizations of personal social networks on Facebook and community structure: an exploratory study

2017 ◽  
Author(s):  
Christina Gkini ◽  
Alexios Brailas
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Single Point ◽  
Community Formation ◽  
Structure Pattern ◽  
Diffusion Of Information ◽  
Qualitative Interpretation ◽  
Network Topologies ◽  
Personal Social Networks ◽  
And Diffusion

We studied the community structure pattern in the visualizations of ten personal social networks on Facebook at a single point in time. It seems to be a strong tendency towards community formation in online personal, social networks: somebody’s friends are usually also friends between them, forming subgroups of more densely connected nodes. Research on community structure in social networks usually focuses on the networks’ statistical properties. There is a need for qualitative studies bridging the gap between network topologies and their sociological implications. To this direction, visual representations of personal networks in social media could be a valuable source of empirical data for qualitative interpretation. Most of the personal social networks’ visualizations in the present study are very highly clustered with densely-knit overlapping subgroups of friends and interconnected between them through wide bridges. This network topology pattern seems to be quite efficient, allowing for a fast spread and diffusion of information across the whole social network.

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.

A Study of Friendship Networks and Blogosphere

2010 ◽  
pp. 646-669
Author(s):  
Nitin Agarwal ◽  
Huan Liu ◽  
Jianping Zhang
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Community Formation ◽  
Friendship Networks ◽  
Research Issues ◽  
Collective Wisdom ◽  
Web Technologies ◽  
The Social ◽  
Community Information ◽  
Community Clustering

In Golbeck and Hendler (2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other members. However, for large social friendship networks it is infeasible to assign trust ratings to each and every member so they propose an inferring mechanism which would assign binary trust ratings (trustworthy/non-trustworthy) to those who have not been assigned one. They demonstrate the use of these trust values in e-mail ?ltering application domain and report encouraging results. Authors also assume three crucial properties of trust for their approach to work: transitivity, asymmetry, and personalization. These trust scores are often transitive, meaning, if Alice trusts Bob and Bob trusts Charles then Alice can trust Charles. Asymmetry says that for two people involved in a relationship, trust is not necessarily identical in both directions. This is contrary to what was proposed in Yu and Singh (2003). They assume symmetric trust values in the social friendship network. Social networks allow us to share experiences, thoughts, opinions, and ideas. Members of these networks, in return experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their needs will be met through being together. Individuals expand their social networks, convene groups of like-minded individuals and nurture discussions. In recent years, computers and the World Wide Web technologies have pushed social networks to a whole new level. It has made possible for individuals to connect with each other beyond geographical barriers in a “flat” world. The widespread awareness and pervasive usability of the social networks can be partially attributed to Web 2.0. Representative interaction Web services of social networks are social friendship networks, the blogosphere, social and collaborative annotation (aka “folksonomies”), and media sharing. In this work, we brie?y introduce each of these with focus on social friendship networks and the blogosphere. We analyze and compare their varied characteristics, research issues, state-of-the-art approaches, and challenges these social networking services have posed in community formation, evolution and dynamics, emerging reputable experts and in?uential members of the community, information diffusion in social networks, community clustering into meaningful groups, collaboration recommendation, mining “collective wisdom” or “open source intelligence” from the exorbitantly available user-generated contents. We present a comparative study and put forth subtle yet essential differences of research in friendship networks and Blogosphere, and shed light on their potential research directions and on cross-pollination of the two fertile domains of ever expanding social networks on the Web.

A Study of Friendship Networks and Blogosphere

2011 ◽  
pp. 661-684
Author(s):  
Nitin Agarwal ◽  
Huan Liu ◽  
Jianping Zhang
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Community Formation ◽  
Friendship Networks ◽  
Research Issues ◽  
Collective Wisdom ◽  
Web Technologies ◽  
The Social ◽  
Community Information ◽  
Community Clustering

In (Golbeck and Hendler, 2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other members. However, for large social friendship networks it is infeasible to assign trust ratings to each and every member so they propose an inferring mechanism which would assign binary trust ratings (trustworthy/non-trustworthy) to those who have not been assigned one. They demonstrate the use of these trust values in email filtering application domain and report encouraging results. Authors also assume three crucial properties of trust for their approach to work; transitivity, asymmetry, and personalization. These trust scores are often transitive, meaning, if Alice trusts Bob and Bob trusts Charles then Alice can trust Charles. Asymmetry says that for two people involved in a relationship, trust is not necessarily identical in both directions. This is contrary to whatwas proposed in (Yu and Singh, 2003). They assume symmetric trust values in the social friendship network. Social networks allow us to share experiences, thoughts, opinions, and ideas. Members of these networks, in return experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their needs will be met through being together. Individuals expand their social networks, convene groups of like-minded individuals and nurture discussions. In recent years, computers and the World Wide Web technologies have pushed social networks to a whole new level. It has made possible for individuals to connect with each other beyond geographical barriers in a “flat” world. The widespread awareness and pervasive usability of the social networks can be partially attributed to Web 2.0. Representative interaction Web services of social networks are social friendship networks, the blogosphere, social and collaborative annotation (aka “folksonomies”), and media sharing. In this work, we briefly introduce each of these with focus on social friendship networks and the blogosphere. We analyze and compare their varied characteristics, research issues, state-of-the-art approaches, and challenges these social networking services have posed in community formation, evolution and dynamics, emerging reputable experts and influential members of the community, information diffusion in social networks, community clustering into meaningful groups, collaboration recommendation, mining “collective wisdom” or “open source intelligence” from the exorbitantly available user-generated contents. We present a comparative study and put forth subtle yet essential differences of research in friendship networks and Blogosphere, and shed light on their potential research directions and on cross-pollination of the two fertile domains of ever expanding social networks on the Web.

Social networks and language change in Tudor and Stuart London – only connect?

2015 ◽  
Vol 19 (2) ◽  
pp. 269-292 ◽  
Author(s):  
TERTTU NEVALAINEN
Keyword(s):  
Social Networks ◽  
Seventeenth Century ◽  
Sixteenth Century ◽  
Network Analysis ◽  
Early Modern ◽  
Language Change ◽  
Linguistic Terms ◽  
Linguistic Change ◽  
Exchange Network ◽  
The City

Place is an integral part of social network analysis, which reconstructs network structures and documents the network members’ linguistic practices in a community. Historical network analysis presents particular challenges in both respects. This article first discusses the kinds of data, official documents, personal letters and diaries that historians have used in reconstructing social networks and communities. These analyses could be enriched by including linguistic data and, vice versa, historical sociolinguistic findings may often be interpreted in terms of social networks.Focusing on Early Modern London, I present two case studies, the first one investigating a sixteenth-century merchant family exchange network and the second discussing the seventeenth-century naval administrator Samuel Pepys, whose role as a community broker between the City and Westminster is assessed in linguistic terms. My results show how identifying the leaders and laggers of linguistic change can add to our understanding of the varied ways in which linguistic innovations spread to and from Tudor and Stuart London both within and across social networks.

scholarly journals Epistemic Communities under Active Inference

2022 ◽  
Author(s):  
Mahault Albarracin ◽  
Daphne Demekas ◽  
Maxwell Ramstead ◽  
Conor Heins
Keyword(s):  
Social Networks ◽  
Confirmation Bias ◽  
Community Formation ◽  
Epistemic Communities ◽  
Local Network ◽  
Active Inference ◽  
Shared Beliefs ◽  
The World ◽  
Echo Chambers ◽  
Degree Of Certainty

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between agent's beliefs and its observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviors of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g. `tweets') while reading the socially-observable claims of other agents, that lend support towards one of two mutually-exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated simulated using the freely-available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

A Study of Friendship Networks and Blogosphere

2009 ◽  
pp. 1078-1100
Author(s):  
Nitin Agarwal ◽  
Huan Liu ◽  
Jianping Zhang
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Community Formation ◽  
Friendship Networks ◽  
Research Issues ◽  
Collective Wisdom ◽  
Web Technologies ◽  
The Social ◽  
Community Information ◽  
Community Clustering

In Golbeck and Hendler (2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other members. However, for large social friendship networks it is infeasible to assign trust ratings to each and every member so they propose an inferring mechanism which would assign binary trust ratings (trustworthy/non-trustworthy) to those who have not been assigned one. They demonstrate the use of these trust values in e-mail ?ltering application domain and report encouraging results. Authors also assume three crucial properties of trust for their approach to work: transitivity, asymmetry, and personalization. These trust scores are often transitive, meaning, if Alice trusts Bob and Bob trusts Charles then Alice can trust Charles. Asymmetry says that for two people involved in a relationship, trust is not necessarily identical in both directions. This is contrary to what was proposed in Yu and Singh (2003). They assume symmetric trust values in the social friendship network. Social networks allow us to share experiences, thoughts, opinions, and ideas. Members of these networks, in return experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their needs will be met through being together. Individuals expand their social networks, convene groups of like-minded individuals and nurture discussions. In recent years, computers and the World Wide Web technologies have pushed social networks to a whole new level. It has made possible for individuals to connect with each other beyond geographical barriers in a “flat” world. The widespread awareness and pervasive usability of the social networks can be partially attributed to Web 2.0. Representative interaction Web services of social networks are social friendship networks, the blogosphere, social and collaborative annotation (aka “folksonomies”), and media sharing. In this work, we brie?y introduce each of these with focus on social friendship networks and the blogosphere. We analyze and compare their varied characteristics, research issues, state-of-the-art approaches, and challenges these social networking services have posed in community formation, evolution and dynamics, emerging reputable experts and in?uential members of the community, information diffusion in social networks, community clustering into meaningful groups, collaboration recommendation, mining “collective wisdom” or “open source intelligence” from the exorbitantly available user-generated contents. We present a comparative study and put forth subtle yet essential differences of research in friendship networks and Blogosphere, and shed light on their potential research directions and on cross-pollination of the two fertile domains of ever expanding social networks on the Web.

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.

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