Cohesive and connected communities create resilience

The following article discusses the “real” communication and the question of “pure” information. In this paper the effective communication is based on a complex exchange of different kinds of messages, both verbal and non-verbal, which include information conveyed by posture, gestures, intonation, facial expression, and so on. Moreover, interlocutors do not only exchange messages with strategic information and structured data; they also exchange free content messages which are extremely important for the interaction and the relationship between the people involved environments.

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Historians, Community, and the Pursuit of Jefferson: Comment on Professor Tomlins

Studies in American Political Development ◽

10.1017/s0898588x00000870 ◽

1990 ◽

Vol 4 ◽

pp. 35-44 ◽

Cited By ~ 1

Author(s):

Joyce Appleby

Keyword(s):

United States ◽

Nineteenth Century ◽

Rural Society ◽

The United States ◽

Industrial Nation ◽

Catch Up ◽

Connected Communities

The “tions” came to the United States in the closing decades of the nineteenth century: industrialization, urbanization, immigration, centralization, and bureaucratization. As befits such an impersonal suffix, these developments have been analyzed as parts of a systematic reorganization of society. Processes, not persons, have figured as the sources of motivation in the story of America's transformation from a rural society of loosely connected communities to an industrial nation integrated by corporations, communications, and the regulations of a government trying to catch up with the pace of change.

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Human Dynamics Research in Smart and Connected Communities

10.1007/978-3-319-73247-3 ◽

2018 ◽

Cited By ~ 5

Keyword(s):

Human Dynamics ◽

Connected Communities

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CONNECTED COMMUNITIES:

Beyond Alterity ◽

10.2307/j.ctt20fw7cq.6 ◽

2018 ◽

pp. 61-83

Author(s):

PETER GUARDINO

Keyword(s):

Connected Communities

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Multiobjective Group Search Optimization Approach for Community Detection in Networks

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2016070103 ◽

2016 ◽

Vol 7 (3) ◽

pp. 50-70 ◽

Cited By ~ 1

Author(s):

Nidhi Arora ◽

Hema Banati

Keyword(s):

Community Detection ◽

Optimization Techniques ◽

Optimization Approach ◽

Multiple Features ◽

Detection Algorithms ◽

Search Optimization ◽

Art Community ◽

Group Search ◽

Single Objective ◽

Connected Communities

Various evolving approaches have been extensively applied to evolve densely connected communities in complex networks. However these techniques have been primarily single objective optimization techniques, which optimize only a specific feature of the network missing on other important features. Multiobjective optimization techniques can overcome this drawback by simultaneously optimizing multiple features of a network. This paper proposes MGSO, a multiobjective variant of Group Search Optimization (GSO) algorithm to globally search and evolve densely connected communities. It uses inherent animal food searching behavior of GSO to simultaneously optimize two negatively correlated objective functions and overcomes the drawbacks of single objective based CD algorithms. The algorithm reduces random initializations which results in fast convergence. It was applied on 6 real world and 33 synthetic network datasets and results were compared with varied state of the art community detection algorithms. The results established show the efficacy of MGSO to find accurate community structures.

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