Modeling Memetics Using Edge Diversity

Gupta, Yayati; Saxena, Akrati; Das, Debarati; Iyengar, S. R. S.

doi:10.1007/978-3-319-30569-1_14

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…The similar results are obtained for the networks of different sizes and densities. Real-world networks posses meso-scale structures like community and coreperiphery structure [24,25]. We also simulate the proposed model on the following real-world networks.…”

Section: Discussionmentioning

confidence: 99%

Social Network Analysis of the Caste-Based Reservation System in India

Saxena¹,

Saini²,

Gupta³

et al. 2015

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It has been argued that the reservation system in India, which has existed since the time of Indian Independence (1947), has caused more havoc and degradation than progress. This being a popular public opinion, has not been based on any rigorous scientific study or research. In this paper, we revisit the cultural divide among the Indian population from a purely social network based approach. We study the distinct cluster formation that takes place in the Indian community and find that this is largely due to the effect of caste-based homophily. To study the impact of the reservation system, we define a new parameter called social distance that represents the social capital associated with each individual in the backward class. We study the changes that take place with regard to the average social distance of a cluster when a new link is established between the clusters which in its essence, is what the reservation system is accomplishing. Our extensive study calls for the change in the mindset of people in India. Although the animosity towards the reservation system could be rooted due to historical influence, hero worship and herd mentality, our results make it clear that the system has had a considerable impact on the country's overall development by bridging the gap between the conflicting social groups. The results also have been verified using the survey and are discussed in the paper.

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Section: Discussionmentioning

confidence: 99%

Social Network Analysis of the Caste-Based Reservation System in India

Saxena¹,

Saini²,

Gupta³

et al. 2015

Preprint

Self Cite

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“…As observed in Figure 7, the intra-community results are less affected by the number of walks than the inter-community links as the ratio of inter-community context pairs decreases with more number of walks; as we expected. Similarly, the inter-community accuracy also decreases with the walk-length even if the total accuracy is improved, as shown in Figure 8 We compare the running time of different network embedding based methods on synthetic networks generated using SCCP (Scale-free networks with Community and Core-Periphery) model [50,51]. The network generator first creates a seed graph, i.e., a complete graph of m nodes for each community, where m is the average degree of nodes.…”

Section: Parameter Sensitivitymentioning

confidence: 98%

NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction

Saxena¹,

Fletcher²,

Pechenizkiy³

2021

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In real-world complex networks, understanding the dynamics of their evolution has been of great interest to the scientific community. Predicting future links is an essential task of social network analysis as the addition or removal of the links over time leads to the network evolution. In a network, links can be categorized as intra-community links if both end nodes of the link belong to the same community, otherwise inter-community links. The existing link-prediction methods have mainly focused on achieving high accuracy for intra-community link prediction. In this work, we propose a network embedding method, called NodeSim, which captures both similarities between the nodes and the community structure while learning the low-dimensional representation of the network. The embedding is learned using the proposed NodeSim random walk, which efficiently explores the diverse neighborhood while keeping the more similar nodes closer in the context of the node. We verify the efficacy of the proposed embedding method over state-of-the-art methods using diverse link prediction. We propose a machine learning model for link prediction that considers both the nodes' embedding and their community information to predict the link between two given nodes. Extensive experimental results on several real-world networks demonstrate the effectiveness of the proposed framework for both inter and intra-community link prediction.

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“…Therefore, our approach also builds on the memetics literature, among others, especially when memes are understood as units of information transmitted primarily via social learning processes (e.g., Heylighen and Chielens 2009; von Bülow 2013, for an overview). 6 In this sense, ideas or units of knowledge may also be conceived as memes "made" of (semantic) information (Dennett 1995(Dennett , 2017) that can diffuse through or on social networks of agents (e.g., Gupta et al 2016;Spitzberg 2014; Weng 2014). 7 More importantly, the memetics literature also supports the idea of representing knowledge as a network based on the idea of memeplexes (e.g., Speel 1999), which may be conceived as complex systems or complex networks of memes that can replicate more successfully in an aggregated or connected form than the isolated memes on their own (Blackmore 1999;Heylighen and Chielens 2009;.…”

Section: Motivation and Foundationsmentioning

confidence: 99%