scholarly journals Link Prediction with Spatial and Temporal Consistency in Dynamic Networks

2017 ◽  
Author(s):  
Wenchao Yu ◽  
Wei Cheng ◽  
Charu C Aggarwal ◽  
Haifeng Chen ◽  
Wei Wang
Keyword(s):  
Link Prediction ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Evolution ◽  
Generic Model ◽  
Temporal Consistency ◽  
Temporal Network ◽  
Close Proximity ◽  
Two Factors ◽  
Random Node

Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted tremendous research interests. Many models have been developed to predict links that may emerge in the immediate future from the past evolution of the networks. There are two key factors: 1) a node is more likely to form a link in the near future with another node within its close proximity, rather than with a random node; 2) a dynamic network usually evolves smoothly. Existing approaches seldom unify these two factors to strive for the spatial and temporal consistency in a dynamic network. To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time. LIST characterizes the network dynamics as a function of time, which integrates the spatial topology of network at each timestamp and the temporal network evolution. Comparing to existing approaches, LIST has two advantages: 1) LIST uses a generic model to express the network structure as a function of time, which makes it also suitable for a wide variety of temporal network analysis problems beyond the focus of this paper; 2) by retaining the spatial and temporal consistency, LIST yields better prediction performance. Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model.

Similarity-Based Indices or Metrics for Link Prediction

2019 ◽  
pp. 1-29
Author(s):  
Praveen Kumar Bhanodia ◽  
Kamal Kumar Sethi ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Online Social Network ◽  
Dynamic Networks ◽  
Relevant Information ◽  
Network Evolution ◽  
Problem Instance ◽  
The Social ◽  
Social Network Evolution

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.

scholarly journals DNA-based constitutional dynamic networks as functional modules for logic gates and computing circuit operations

2021 ◽  
Vol 12 (15) ◽  
pp. 5473-5483
Author(s):  
Zhixin Zhou ◽  
Jianbang Wang ◽  
R. D. Levine ◽  
Francoise Remacle ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Logic Gates ◽  
Functional Modules ◽  
Logic Operations

A nucleic acid-based constitutional dynamic network (CDN) provides a single functional computational module for diverse input-guided logic operations and computing circuits.

Network embedding based link prediction in dynamic networks

2021 ◽  
Author(s):  
Shashi Prakash Tripathi ◽  
Rahul Kumar Yadav ◽  
Abhay Kumar Rai
Keyword(s):  
Link Prediction ◽  
Dynamic Networks ◽  
Network Embedding

scholarly journals Assessment of crop and property damage caused by Semnopithecus vetulus nestor (Bennett, 1833) (Mammalia: Primates: Cercopithecidae) in Gampaha District, Sri Lanka

2021 ◽  
Vol 13 (5) ◽  
pp. 18141-18147
Author(s):  
Sunil Wijethilaka ◽  
Lakshani S. Weerasekara ◽  
Saumya Bandara ◽  
Kithsiri B. Ranawana
Keyword(s):  
Sri Lanka ◽  
Ornamental Plants ◽  
Sri Lankan ◽  
Property Damage ◽  
Severe Problem ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Close Proximity ◽  
Two Factors ◽  
Semnopithecus Vetulus Nestor

In earlier times, human-monkey interactions were not a severe problem in Sri Lanka, but has recently intensified as a result of habitat fragmentation and urbanization.  Due to these changes, Semnopithecus vetulus nestor has been listed among the 25 most Endangered primates.  The objective of our study was to evaluate the intensity of human-S.v. nestor negative interaction by identifying the crop and property damages in villages bordering Danawkanda Forest (7.001N & 80.049E), Gampaha, Sri Lanka.  We collected data using structured questionnaires interviewing households (N= 80) bordering the Danawkanda Forest from August 2014 to January 2015.  Households were most affected by damage to fruits, leaves, and buds of commercially important trees (93%), followed by damage to roof tiles (76%), and frightful confrontations with the monkeys (43%).  Average monthly loss per household from crop and property damage was estimated at between (Sri Lankan Rupees) LKR 2,700 and LKR 1,500.  Lighting firecrackers was the most common method used by the residents (99%) to deter monkeys, where as electrified barriers were rarely used (4%).  Households in close proximity to Danawkanda Forest experienced a considerable loss to their monthly income due to crop and property damage, compared to households further away.  As an alternative, residents now grow ornamental plants and short trees, eliminating the structures that attract and facilitate damage by S.v. nestor.  Awareness and active participation of residents, authorized government, and non-governmental organizations are needed to manage unplanned construction and agriculture plot extensions into the forest.  These two factors trigger the human-wildlife negative interactions in general and are not limited just to monkeys.

scholarly journals Deep Dynamic Network Embedding for Link Prediction

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29219-29230 ◽  
Author(s):  
Taisong Li ◽  
Jiawei Zhang ◽  
Philip S. Yu ◽  
Yan Zhang ◽  
Yonghong Yan
Keyword(s):  
Link Prediction ◽  
Dynamic Network ◽  
Network Embedding

An experimental evaluation and guideline for path finding in weighted dynamic network

2021 ◽  
Vol 14 (11) ◽  
pp. 2127-2140
Author(s):  
Mengxuan Zhang ◽  
Lei Li ◽  
Xiaofang Zhou
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Real Life ◽  
Dynamic Network ◽  
Problem Space ◽  
Network Applications ◽  
Complete Problem ◽  
Path Computation ◽  
Shortest Path Computation

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the Dynamic Shortest Path (DSP) problem has drawn lots of attention in recent years. However, as DSP has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the determinant dimensions and constraint dimensions of the DSP problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art DSP methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of dynamic degree to classify the dynamic environments and use throughput to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.

A transportation network evolution model based on link prediction

2021 ◽  
Author(s):  
Shuang Gu ◽  
Keping Li ◽  
Yan Liang ◽  
Dongyang Yan
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Transportation Networks ◽  
Network Evolution ◽  
Evolution Model ◽  
Civil Aviation ◽  
Node Degree ◽  
Network Efficiency ◽  
Evolution Mechanism ◽  
Proposed Model

An effective and reliable evolution model can provide strong support for the planning and design of transportation networks. As a network evolution mechanism, link prediction plays an important role in the expansion of transportation networks. Most of the previous algorithms mainly took node degree or common neighbors into account in calculating link probability between two nodes, and the structure characteristics which can enhance global network efficiency are rarely considered. To address these issues, we propose a new evolution mechanism of transportation networks from the aspect of link prediction. Specifically, node degree, distance, path, expected network structure, relevance, population and GDP are comprehensively considered according to the characteristics and requirements of the transportation networks. Numerical experiments are done with China’s high-speed railway network, China’s highway network and China’s inland civil aviation network. We compare receiver operating characteristic curve and network efficiency in different models and explore the degree and hubs of networks generated by the proposed model. The results show that the proposed model has better prediction performance and can effectively optimize the network structure compared with other baseline link prediction methods.

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