Parallels in Neural and Human Communication Networks

2013 ◽  
pp. 39-49
Author(s):  
L. J. Larson-Prior
Keyword(s):  
Communication Networks ◽  
Human Communication

scholarly journals Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

2018 ◽  
Vol 115 (7) ◽  
pp. 1433-1438 ◽  
Author(s):  
Tim Gernat ◽  
Vikyath D. Rao ◽  
Martin Middendorf ◽  
Harry Dankowicz ◽  
Nigel Goldenfeld ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Communication Networks ◽  
Disease Transmission ◽  
Large Scale ◽  
Reference Network ◽  
Interaction Patterns ◽  
Human Communication ◽  
Automated Monitoring ◽  
Phone Calls

Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.

Detecting community structure by belonging intensity analysis of intermediate nodes

2019 ◽  
Vol 30 (11) ◽  
pp. 1950079
Author(s):  
Mengjia Shen ◽  
Dong Lv ◽  
Zhixin Ma
Keyword(s):  
Community Structure ◽  
Communication Networks ◽  
Community Detection ◽  
Real World ◽  
Intensity Analysis ◽  
Human Communication ◽  
Intermediate Node ◽  
Detection Algorithms ◽  
Interactive Behavior

Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

scholarly journals Structure and tie strengths in mobile communication networks

2007 ◽  
Vol 104 (18) ◽  
pp. 7332-7336 ◽  
Author(s):  
J.-P. Onnela ◽  
J. Saramäki ◽  
J. Hyvönen ◽  
G. Szabó ◽  
D. Lazer ◽  
...  
Keyword(s):  
Phase Transition ◽  
Communication Networks ◽  
Mobile Communication ◽  
Local Structure ◽  
Information Diffusion ◽  
Communication Patterns ◽  
Weak Ties ◽  
Human Communication ◽  
Strong Ties ◽  
Mobile Communication Networks

Electronic databases, from phone to e-mails logs, currently provide detailed records of human communication patterns, offering novel avenues to map and explore the structure of social and communication networks. Here we examine the communication patterns of millions of mobile phone users, allowing us to simultaneously study the local and the global structure of a society-wide communication network. We observe a coupling between interaction strengths and the network's local structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties but fall apart after a phase transition if the weak ties are removed. We show that this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities and find that, when it comes to information diffusion, weak and strong ties are both simultaneously ineffective.

Game Theory in Wireless and Communication Networks

2009 ◽  
Author(s):  
Zhu Han ◽  
Dusit Niyato ◽  
Walid Saad ◽  
Tamer Basar ◽  
Are Hjorungnes
Keyword(s):  
Game Theory ◽  
Communication Networks

Investigating the Multimodal Nature of Human Communication

2009 ◽  
Vol 23 (2) ◽  
pp. 63-76 ◽  
Author(s):  
Silke Paulmann ◽  
Sarah Jessen ◽  
Sonja A. Kotz
Keyword(s):  
Visual Information ◽  
Empirical Studies ◽  
Physical Property ◽  
Event Related Potentials ◽  
Accurate Information ◽  
Human Communication ◽  
Channel Condition ◽  
Prosodic Cues ◽  
Early Processing ◽  
Related Potentials

The multimodal nature of human communication has been well established. Yet few empirical studies have systematically examined the widely held belief that this form of perception is facilitated in comparison to unimodal or bimodal perception. In the current experiment we first explored the processing of unimodally presented facial expressions. Furthermore, auditory (prosodic and/or lexical-semantic) information was presented together with the visual information to investigate the processing of bimodal (facial and prosodic cues) and multimodal (facial, lexic, and prosodic cues) human communication. Participants engaged in an identity identification task, while event-related potentials (ERPs) were being recorded to examine early processing mechanisms as reflected in the P200 and N300 component. While the former component has repeatedly been linked to physical property stimulus processing, the latter has been linked to more evaluative “meaning-related” processing. A direct relationship between P200 and N300 amplitude and the number of information channels present was found. The multimodal-channel condition elicited the smallest amplitude in the P200 and N300 components, followed by an increased amplitude in each component for the bimodal-channel condition. The largest amplitude was observed for the unimodal condition. These data suggest that multimodal information induces clear facilitation in comparison to unimodal or bimodal information. The advantage of multimodal perception as reflected in the P200 and N300 components may thus reflect one of the mechanisms allowing for fast and accurate information processing in human communication.

Review of Understanding Human Communication (3rd ed.).

1988 ◽  
Vol 33 (10) ◽  
pp. 920-921
Author(s):  
L. Kristine Pond
Keyword(s):  
Human Communication

Effective human to human communication of information provided by an unmanned vehicle

2005 ◽  
Author(s):  
Patricia L. McDermott ◽  
Jason Luck ◽  
Laurel Allender ◽  
Alia Fisher
Keyword(s):  
Human Communication ◽  
Unmanned Vehicle
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