Hierarchical core maintenance on large dynamic graphs

2021 ◽  
Vol 14 (5) ◽  
pp. 757-770
Author(s):  
Zhe Lin ◽  
Fan Zhang ◽  
Xuemin Lin ◽  
Wenjie Zhang ◽  
Zhihong Tian
Keyword(s):  
Social Networks ◽  
Network Analysis ◽  
Real World ◽  
World Wide ◽  
Real Life ◽  
Dynamic Graphs ◽  
Large Graphs ◽  
Structure Changes ◽  
The World ◽  
Graph Dynamics

The model of k -core and its decomposition have been applied in various areas, such as social networks, the world wide web, and biology. A graph can be decomposed into an elegant k -core hierarchy to facilitate cohesive subgraph discovery and network analysis. As many real-life graphs are fast evolving, existing works proposed efficient algorithms to maintain the coreness value of every vertex against structure changes. However, the maintenance of the k -core hierarchy in existing studies is not complete because the connections among different k -cores in the hierarchy are not considered. In this paper, we study hierarchical core maintenance which is to compute the k -core hierarchy incrementally against graph dynamics. The problem is challenging because the change of hierarchy may be large and complex even for a slight graph update. In order to precisely locate the area affected by graph dynamics, we conduct in-depth analyses on the structural properties of the hierarchy, and propose well-designed local update techniques. Our algorithms significantly outperform the baselines on runtime by up to 3 orders of magnitude, as demonstrated on 10 real-world large graphs.

scholarly journals Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 361 ◽  
Author(s):  
Raji Ghawi ◽  
Jürgen Pfeffer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Social Relations ◽  
World Wide ◽  
Open Data ◽  
Linked Open Data ◽  
The World ◽  
Description Framework ◽  
Resource Description

Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure.

Stigmergic Hyperlink

2012 ◽  
pp. 260-272
Author(s):  
Artur Sancho Marques ◽  
José Figueiredo
Keyword(s):  
Social Networks ◽  
Social Change ◽  
World Wide Web ◽  
World Wide ◽  
Service Providers ◽  
Information Service ◽  
Web Page ◽  
The World ◽  
Patterns Of Behavior ◽  
Ecological Treatment

Inspired by patterns of behavior generated in social networks, a prototype of a new object was designed and developed for the World Wide Web – the stigmergic hyperlink or “stigh”. In a system of stighs, like a Web page, the objects that users do use grow “healthier”, while the unused “weaken”, eventually to the extreme of their “death”, being autopoieticaly replaced by new destinations. At the single Web page scale, these systems perform like recommendation systems and embody an “ecological” treatment to unappreciated links. On the much wider scale of generalized usage, because each stigh has a method to retrieve information about its destination, Web agents in general and search engines in particular, would have the option to delegate the crawling and/or the parsing of the destination. This would be an interesting social change: after becoming not only consumers, but also content producers, Web users would, just by hosting (automatic) stighs, become information service providers too.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

scholarly journals Mathematics of Web science: structure, dynamics and incentives

2013 ◽  
Vol 371 (1987) ◽  
pp. 20120377 ◽  
Author(s):  
Jennifer Chayes
Keyword(s):  
Autonomous System ◽  
World Wide ◽  
Autonomous Systems ◽  
Large Graphs ◽  
Finite Graphs ◽  
Online Networks ◽  
The People ◽  
Web Science ◽  
The World ◽  
Online Experience

Dr Chayes’ talk described how, to a discrete mathematician, ‘all the world’s a graph, and all the people and domains merely vertices’. A graph is represented as a set of vertices V and a set of edges E, so that, for instance, in the World Wide Web, V is the set of pages and E the directed hyperlinks; in a social network, V is the people and E the set of relationships; and in the autonomous system Internet, V is the set of autonomous systems (such as AOL, Yahoo! and MSN) and E the set of connections. This means that mathematics can be used to study the Web (and other large graphs in the online world) in the following way: first, we can model online networks as large finite graphs; second, we can sample pieces of these graphs; third, we can understand and then control processes on these graphs; and fourth, we can develop algorithms for these graphs and apply them to improve the online experience.

Local community detection based on modularity metric G

2015 ◽  
Vol 29 (33) ◽  
pp. 1550215 ◽  
Author(s):  
Zhengyou Xia ◽  
Xiangying Gao ◽  
Xia Zhang
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
World Wide ◽  
Local Community ◽  
Complete Information ◽  
Detection Problem ◽  
Two Phase ◽  
The World ◽  
Local Community Detection

In complex network analysis, the local community detection problem is getting more and more attention. Because of the difficulty to get complete information of the network, such as the World Wide Web, the local community detection has been proposed by researcher. That is, we can detect a community from a certain source vertex with limited knowledge of an entire graph. The previous methods of local community detection now are more or less inadequate in some places. In this paper, we have proposed a new local modularity metric [Formula: see text] and based on it, a two-phase algorithm is proposed. The method we have taken is a greedy addition algorithm which means adding vertices into the community until [Formula: see text] does not increase. Compared with the previous methods, when our method is calculating the modularity metric, the range of vertices what we considered may affect the quality of the community detection wider. The results of experiments show that whether in computer-generated random graph or in the real networks, our method can effectively solve the problem of the local community detection.

Data Mining for Social Network Analysis Using a CLIQUE Algorithm

2019 ◽  
pp. 160-187
Author(s):  
Phu Ngoc Vo ◽  
Tran Vo Thi Ngoc
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
The World ◽  
Commercial Applications ◽  
Clique Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.

Software for Membrane Computing

2020 ◽  
pp. 659-678
Author(s):  
Andrei George Florea ◽  
Cătălin Buiu
Keyword(s):  
Real World ◽  
Membrane Computing ◽  
Real Life ◽  
Natural Computing ◽  
Software Application ◽  
The World ◽  
Definition Of ◽  
And Robotics ◽  
Computing Models ◽  
Detailed Presentation

In order to use membrane computing models for real life applications there is a real need for software that can read a model from some form of input media and afterwards execute it according to the execution rules that are specified in the definition of the model. Another requirement of this software application is for it to be capable of interfacing the computing model with the real world. This chapter discusses how this problem was solved along the years by various researchers around the world. After presenting notable examples from the literature, the discussion continues with a detailed presentation of three membrane computing simulators that have been developed by the authors at the Laboratory of Natural Computing and Robotics at the Politehnica University of Bucharest, Romania.

Key differences between Web 1.0 and Web 2.0

First Monday ◽  
2008 ◽  
Author(s):  
Graham Cormode ◽  
Balachander Krishnamurthy
Keyword(s):  
Social Networks ◽  
Critical Thinking ◽  
World Wide Web ◽  
Web 2.0 ◽  
World Wide ◽  
New Technologies ◽  
User Interaction ◽  
Past Work ◽  
Technical Work ◽  
The World

Web 2.0 is a buzzword introduced in 2003-04 which is commonly used to encompass various novel phenomena on the World Wide Web. Although largely a marketing term, some of the key attributes associated with Web 2.0 include the growth of social networks, bi-directional communication, various 'glue' technologies, and significant diversity in content types. We are not aware of a technical comparison between Web 1.0 and 2.0. While most of Web 2.0 runs on the same substrate as 1.0, there are some key differences. We capture those differences and their implications for technical work in this paper. Our goal is to identify the primary differences leading to the properties of interest in 2.0 to be characterized. We identify novel challenges due to the different structures of Web 2.0 sites, richer methods of user interaction, new technologies, and fundamentally different philosophy. Although a significant amount of past work can be reapplied, some critical thinking is needed for the networking community to analyze the challenges of this new and rapidly evolving environment.

scholarly journals Student Perceptions of Problem-Based Learning: A Case Study of Undergraduate Applied Agrometeorology

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Linda De Wet ◽  
Sue Walker
Keyword(s):  
Critical Thinking ◽  
Student Perceptions ◽  
Real World ◽  
Formal Education ◽  
Real Life ◽  
Problem Based Learning ◽  
Ongoing Study ◽  
Weather Data ◽  
The World

Many students do not seem to transfer their learning during formal education into applications in the real world. The objective of this ongoing study was to investigate the opinion of third-year students concerning their program through problem-based learning and to improve the module where necessary. Students attending theory classes had to apply their newly gained knowledge coupled with real-life weather data to solve a problem during practicums. Students attending practicums were given the same questionnaire thrice; thus, the answers were based on different sets of exercises. Responses by attendees for the three questionnaires were 73%, 100%, and 61%, respectively. Students preferred problem-based practicums (78%, 54%, and 72%, resp.) to other non-problem-based practicums. Most students thought that their knowledge had improved and it had prepared them better for the workplace (85%, 77%, and 92%, resp.). Generally students preferred working in groups (74%, 62%, and 56%, resp.), in contrast to those preferring to work individually. Students benefited from problem-based learning in that they thought they had improved their knowledge, skills, and critical thinking abilities and felt that they had learnt things that they could carry into their future lives out in the world at large and the workplace.

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