Synonymy Expansion Using Link Prediction Methods: A Case Study of Assamese WordNet

2022 ◽  
Vol 21 (1) ◽  
pp. 1-21
Author(s):  
Bornali Phukon ◽  
Akash Anil ◽  
Sanasam Ranbir Singh ◽  
Priyankoo Sarmah
Keyword(s):  
Network Analysis ◽  
Target Word ◽  
Complex Network ◽  
Link Prediction ◽  
Expansion Method ◽  
Prediction Methods ◽  
Network Embedding ◽  
Per Se ◽  
Local Proximity

WordNets built for low-resource languages, such as Assamese, often use the expansion methodology. This may result in missing lexical entries and missing synonymy relations. As the Assamese WordNet is also built using the expansion method, using the Hindi WordNet, it also has missing synonymy relations. As WordNets can be visualized as a network of unique words connected by synonymy relations, link prediction in complex network analysis is an effective way of predicting missing relations in a network. Hence, to predict the missing synonyms in the Assamese WordNet, link prediction methods were used in the current work that proved effective. It is also observed that for discovering missing relations in the Assamese WordNet, simple local proximity-based methods might be more effective as compared to global and complex supervised models using network embedding. Further, it is noticed that though a set of retrieved words are not synonyms per se, they are semantically related to the target word and may be categorized as semantic cohorts.

Link prediction based on contribution of neighbors

2020 ◽  
Vol 31 (11) ◽  
pp. 2050158
Author(s):  
Xiang-Chun Liu ◽  
Dian-Qing Meng ◽  
Xu-Zhen Zhu ◽  
Yang Tian
Keyword(s):  
Complex Network ◽  
Link Prediction ◽  
Node Degree ◽  
Prediction Methods ◽  
Obvious Advantage ◽  
Node Similarity ◽  
The Difference

Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms.

A Case Study on Citation Network Analysis

2018 ◽  
pp. 171-188
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Citation Network ◽  
Citation Network Analysis ◽  
Document Collection ◽  
Potential Benefits ◽  
Study Case ◽  
Scientific Citation ◽  
Network Metrics

In this chapter, the authors present a case study of Network Analysis in the field of bibliometrics, focused on the identification of central academic articles based on complex network metrics that can be implemented with algorithms covered throughout this book. The authors analyze a scientific citation network and systematically obtain the most central papers considering different perspectives of the selected document collection. Later, they discuss the potential benefits that the parallel kernels and the topology-aware partitioning algorithms can offer in the context of the presented study case. Finally, the authors summarize this book's main contributions and offer some concluding remarks.

scholarly journals Learning Signed Network Embedding via Graph Attention

2020 ◽  
Vol 34 (04) ◽  
pp. 4772-4779 ◽  
Author(s):  
Yu Li ◽  
Yuan Tian ◽  
Jiawei Zhang ◽  
Yi Chang
Keyword(s):  
Network Analysis ◽  
Real World ◽  
Link Prediction ◽  
Critical Role ◽  
Network Embedding ◽  
Convolutional Networks ◽  
Importance Coefficient ◽  
Signed Network ◽  
Low Dimensional ◽  
Negative Links

Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.

scholarly journals Inheritance and Dissemination of Cultural Collective Memory: An Analysis of a Traditional Festival

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824402090160
Author(s):  
Zhenjie Liao ◽  
Guangquan Dai
Keyword(s):  
Network Analysis ◽  
Collective Memory ◽  
Complex Network ◽  
Hierarchical Structure ◽  
Public Memory ◽  
Field Research ◽  
Analysis Software ◽  
Memory Content ◽  
The Rich

The traditional festivals in urban societies constitute public memory and are essential for constructing a sense of locality. Therefore, it is of great significance to study the collective memory of traditional urban festivals to promote the sense of locality. In this research, the Guangzhou Winter Jasmine Flower Market was selected as a case study. By examining the historical literature, and conducting field research and interviews, the collective memory of the Guangzhou Winter Jasmine Flower Market was classified and sorted from the perspective of the categories of collective memory. A collective memory graph of the Guangzhou Winter Jasmine Flower Market was constructed using Gephi complex network analysis software. By examining the rich memory content and hierarchical structure of the Guangzhou Winter Jasmine Flower Market, we can better understand and foster the function of traditional urban festivals as collective memory and achieve the sustainability of collective memory.

scholarly journals Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS

2020 ◽  
Vol 12 (2) ◽  
pp. 538 ◽  
Author(s):  
Shaopei Chen ◽  
Dachang Zhuang
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Small World ◽  
Scientific Basis ◽  
Transmission Efficiency ◽  
Complex Network Analysis ◽  
Average Travel Time ◽  
Basic Characteristics ◽  
Metro Network

This paper takes the metro network of Guangzhou as a case study, and provides a quantitative analysis of the historical development of the network from 1999 to 2018. Particularly, the evolution of the topological structure of the Guangzhou Metro Network (GMN) is evaluated and characterized through the integration of geographic information system (GIS) and complex network analysis. The results show that: (1) The metro network of Guangzhou possesses the basic characteristics of small-world network, (2) with the development of GMN, the network complexity is increased and the spatial dispersion of the nodes tends to ease, but the average travel time and transfer rate continues to rise up, leading to the decreasing of the network transmission efficiency and the scattering of the nodes, (3) a good fault tolerance of the overall metro network of Guangzhou is revealed, but the spatial variance is observed, (4) the peak of degree centrality (DC) of the nodes is gradually moving northward along “Kecun Station–Guangzhou railway station–Jiahe Wanggang station”, while the peak of betweenness centrality (BC) is changing from “Kecun station” to “Jiahe Wanggang station”, and Jiahe Wanggang station has evolved into the most critical node in the current metro network of Guangzhou. In conclusion, this study should provide the scientific basis and significant decision-making support to the planning and operation management of GMN.

scholarly journals Multiplex-Heterogeneous Network Embedding for Drug Repositioning

2021 ◽  
Author(s):  
Léo Pio-Lopez
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Drug Target ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Network Embedding ◽  
Medical Treatments ◽  
Vector Representations

Drug repositioning (also called drug repurposing) is a strategy for identifying new therapeutic targets for existing drugs. This approach is of great importance in pharmacology as it is a faster and cheaper way to develop new medical treatments. In this paper, we present, to our knowledge, the first application of multiplex-heterogeneous network embedding to drug repositioning. Network embedding learns the vector representations of nodes, opening the whole machine learning toolbox for a wide variety of applications including link prediction, node labelling or clustering. So far, the application of network embedding for drug repositioning focused on heterogeneous networks. Our approach for drug repositioning is based on multiplex-heterogeneous network embedding. Such method allows the richness and complexity of multiplex and heterogeneous networks to be projected in the same vector space. In other words, multiplex-heterogeneous networks aggregate different multi-omics data in the same network representation. We validate the approach on a task of link prediction and on a case study for SARS-CoV2 drug repositioning. Experimental results show that our approach is highly robust and effective for finding new drug-target associations.

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