Use of social network models to understand play partner choice strategies in three primate species

Primates ◽  
2019 ◽  
Vol 60 (3) ◽  
pp. 247-260 ◽  
Author(s):  
Meredith C. Lutz ◽  
Jonah Ratsimbazafy ◽  
Peter G. Judge
Keyword(s):  
Social Network ◽  
Network Models ◽  
Partner Choice ◽  
Primate Species ◽  
Play Partner

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

Time Series Geographic Social Network Dynamic Preference Group Query

2021 ◽  
Vol 13 (4) ◽  
pp. 18-39
Author(s):  
Yinglian Zhou ◽  
Jifeng Chen
Keyword(s):  
Time Series ◽  
Social Network ◽  
Social Impact ◽  
Network Models ◽  
User Preferences ◽  
Network Dynamic ◽  
Tree Index ◽  
Query Algorithm ◽  
Value Model ◽  
Dynamic Preferences

Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in record is designed. UTC-tree avoids traversing all user check-in records in the query, accelerating user dynamic preference evaluation. Finally, the DPG query algorithm is used to implement a well-interacted DPG query system. Through a large number of comparative experiments, the validity of UTC-tree and the scalability of DPG query are verified.

Spreading Activation Connectivity Based Approach to Network Clustering

2018 ◽  
pp. 207-219
Author(s):  
Alexander Troussov ◽  
Sergey Maruev ◽  
Sergey Vinogradov ◽  
Mikhail Zhizhin
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Systems ◽  
Network Models ◽  
Real Data ◽  
Spreading Activation ◽  
Network Clustering ◽  
Clustering Method ◽  
Network Characteristics ◽  
Structure Detection

Techno-social systems generate data, which are rather different, than data, traditionally studied in social network analysis and other fields. In massive social networks agents simultaneously participate in several contexts, in different communities. Network models of many real data from techno-social systems reflect various dimensionalities and rationales of actor's actions and interactions. The data are inherently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

IoT Data Collection Based on Social Network Models

2020 ◽  
Author(s):  
Nataly Zhukova ◽  
Aung Myo Thaw ◽  
Man Tianxing ◽  
Mustafin Nikolay
Keyword(s):  
Social Network ◽  
Data Collection ◽  
Network Models

scholarly journals Flexible decision-making in grooming partner choice in sooty mangabeys and chimpanzees

2018 ◽  
Vol 5 (7) ◽  
pp. 172143 ◽  
Author(s):  
Alexander Mielke ◽  
Anna Preis ◽  
Liran Samuni ◽  
Jan F. Gogarten ◽  
Roman M. Wittig ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Relationships ◽  
Partner Choice ◽  
Primate Species ◽  
Choice Situation ◽  
Decision Making Process ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Multiple Partner ◽  
Sooty Mangabeys

Living in permanent social groups forces animals to make decisions about when, how and with whom to interact, requiring decisions to be made that integrate multiple sources of information. Changing social environments can influence this decision-making process by constraining choice or altering the likelihood of a positive outcome. Here, we conceptualized grooming as a choice situation where an individual chooses one of a number of potential partners. Studying two wild populations of sympatric primate species, sooty mangabeys ( Cercocebus atys atys ) and western chimpanzees ( Pan troglodytes verus ), we tested what properties of potential partners influenced grooming decisions, including their relative value based on available alternatives and the social relationships of potential partners with bystanders who could observe the outcome of the decision. Across 1529 decision events, multiple partner attributes (e.g. dominance ranks, social relationship quality, reproductive state, partner sex) influenced choice. Individuals preferred to initiate grooming with partners of similar global rank, but this effect was driven by a bias towards partners with a high rank compared to other locally available options. Individuals also avoided grooming partners who had strong social relationships with at least one bystander. Results indicated flexible decision-making in grooming interactions in both species, based on a partner's value given the local social environment. Viewing partner choice as a value-based decision-making process allows researchers to compare how different species solve similar social problems.

When females trade grooming for grooming: testing partner control and partner choice models of cooperation in two primate species

2011 ◽  
Vol 81 (6) ◽  
pp. 1223-1230 ◽  
Author(s):  
Cécile Fruteau ◽  
Sylvain Lemoine ◽  
Eléonore Hellard ◽  
Eric van Damme ◽  
Ronald Noë
Keyword(s):  
Choice Models ◽  
Partner Choice ◽  
Primate Species

scholarly journals Using Network Models to Analyze Old Chinese Rhyme Data (用網絡模型來分析古代漢語的韻母數據)

2016 ◽  
Vol 9 (2) ◽  
pp. 218-241 ◽  
Author(s):  
Johann-Mattis List (游函)
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strong Evidence ◽  
Crucial Role ◽  
Quantitative Methods ◽  
Network Models ◽  
Online Application ◽  
Historical Phonology ◽  
Old Chinese

The evidence one can draw from the rhyming behavior of Old Chinese words plays a crucial role for the reconstruction of Old Chinese, and is particularly relevant to recent proposals. Some of these proposals are no longer solely based on the intuition of scholars but also substantiated by statistical arguments that help to assess the probability by which a given set of rhyming instances can be assigned to an established rhyme group. So far, however, quantitative methods were only used to confirm given hypotheses regarding rhyme groups in Old Chinese, and no exploratory analyses that would create hypotheses regarding rhyme groups in a corpus were carried out. This paper presents a new method that models rhyme data as weighted undirected networks. By representing rhyme words as nodes in a network and the frequency of rhymes in a given corpus as links between nodes, rhyme groups can be inferred with help of standard algorithms originally designed for social network analysis. This is illustrated through the construction of a rhyme network from the Book of Odes and comparing the automatically inferred rhyme groups with rhyme groups proposed in the literature. Apart from revealing interesting general properties of rhyme networks in Chinese historical phonology, the analysis provides strong evidence for a coda *-r in Old Chinese. The results of the analysis and the rhyme network of the Book of Odes can be inspected in form of an interactive online application or directly downloaded. 古代漢語的詞語所反映的韻為對上古音系的構擬，特別是對於最近的一些上古漢語構擬系統，異常重要。其中有一些構擬系統不再僅僅靠於學者的直覺，而且還用統計參數證實來評估分韻和派韻的概率。然而，迄今為止，定量方法僅用於確認關於上古韻部的假設，並且沒有進行探索性數據分析來創建初步分韻假設。本文提出了一種將韻母數據模型為加權無向網絡的新方法。此方法將韻母模型為網絡中的頂點，將某個語料庫的合韻率模型為聯頂點的邊緣，用社會網絡分析的標準算法來推斷語料庫所反映的韻母。為了更具體的說明此方法，本文用“詩經”來構建韻母網絡，而且比較自動與學者所推斷的上古韻部。除了揭示古代漢語韻網的一些有趣特點，“詩經”韻網分析了支持上古漢語韻尾* -r的新證據。“詩經”韻網和韻網分析的結果可以用交際在線應用來訪問而下載。(This article is in English.)

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