scholarly journals Detecting Communities in 2-Mode Networks via Fast Nonnegative Matrix Trifactorization

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Liu Yang ◽  
Wang Tao ◽  
Ji Xin-sheng ◽  
Liu Caixia ◽  
Xu Mingyan
Keyword(s):  
Community Structure ◽  
Rapid Development ◽  
Nonnegative Matrix ◽  
Communication Technologies ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Slow Convergence ◽  
Network Information ◽  
Relational Networks ◽  
Multiple Dimensions

With the rapid development of the Internet and communication technologies, a large number of multitype relational networks widely emerge in real world applications. The bipartite network is one representative and important kind of complex networks. Detecting community structure in bipartite networks is crucial to obtain a better understanding of the network structures and functions. Traditional nonnegative matrix factorization methods usually focus on homogeneous networks, and they are subject to several problems such as slow convergence and large computation. It is challenging to effectively integrate the network information of multiple dimensions in order to discover the hidden community structure underlying heterogeneous interactions. In this work, we present a novel fast nonnegative matrix trifactorization (F-NMTF) method to cocluster the 2-mode nodes in bipartite networks. By constructing the affinity matrices of 2-mode nodes as manifold regularizations of NMTF, we manage to incorporate the intratype and intratype information of 2-mode nodes to reveal the latent community structure in bipartite networks. Moreover, we decompose the NMTF problem into two subproblems, which are involved with much less matrix multiplications and achieve faster convergence. Experimental results on synthetic and real bipartite networks show that the proposed method improves the slow convergence of NMTF and achieves high accuracy and stability on the results of community detection.

scholarly journals Multiview Community Discovery Algorithm via Nonnegative Factorization Matrix in Heterogeneous Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Tao ◽  
Liu Yang
Keyword(s):  
Community Structure ◽  
Heterogeneous Networks ◽  
Real World ◽  
Rapid Development ◽  
Complex Structure ◽  
Nonnegative Matrix ◽  
Bipartite Graphs ◽  
Semisupervised Learning ◽  
Detection Methods ◽  
Target Mode

With the rapid development of the Internet and communication technologies, a large number of multimode or multidimensional networks widely emerge in real-world applications. Traditional community detection methods usually focus on homogeneous networks and simply treat different modes of nodes and connections in the same way, thus ignoring the inherent complexity and diversity of heterogeneous networks. It is challenging to effectively integrate the multiple modes of network information to discover the hidden community structure underlying heterogeneous interactions. In our work, a joint nonnegative matrix factorization (Joint-NMF) algorithm is proposed to discover the complex structure in heterogeneous networks. Our method transforms the heterogeneous dataset into a series of bipartite graphs correlated. Taking inspiration from the multiview method, we extend the semisupervised learning from single graph to several bipartite graphs with multiple views. In this way, it provides mutual information between different bipartite graphs to realize the collaborative learning of different classifiers, thus comprehensively considers the internal structure of all bipartite graphs, and makes all the classifiers tend to reach a consensus on the clustering results of the target-mode nodes. The experimental results show that Joint-NMF algorithm is efficient and well-behaved in real-world heterogeneous networks and can better explore the community structure of multimode nodes in heterogeneous networks.

scholarly journals Is it correct to project and detect? How weighting unipartite projections influences community detection

2020 ◽  
Vol 8 (S1) ◽  
pp. S145-S163 ◽  
Author(s):  
Tristan J. B. Cann ◽  
Iain S. Weaver ◽  
Hywel T. P. Williams
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Algorithm Performance ◽  
Projection Scheme ◽  
Community Detection Algorithm ◽  
Experimental Findings

AbstractBipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

scholarly journals Similarity-based local community detection for bipartite networks

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1559-1570 ◽  
Author(s):  
Dongming Chen ◽  
Wei Zhao ◽  
Dongqi Wang ◽  
Xinyu Huang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Partial Information ◽  
Real Data ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Local Community Detection

Local community detection aims to obtain the local communities to which target nodes belong, by employing only partial information of the network. As a commonly used network model, bipartite applies naturally when modeling relations between two different classes of objects. There are three problems to be solved in local community detection, such as initial core node selection, expansion approach and community boundary criteria. In this work, a similarity based local community detection algorithm for bipartite networks (SLCDB) is proposed, and the algorithm can be used to detect local community structure by only using either type of nodes of a bipartite network. Experiments on real data prove that SLCDB algorithms output community structure can achieve a very high modularity which outperforms most existing local community detection methods for bipartite networks.

Technology of animation in multimedia courseware of mechanical drawing

2021 ◽  
pp. 002072092098608
Author(s):  
Yucheng Shen ◽  
Wei Lu
Keyword(s):  
Divergent Thinking ◽  
Rapid Development ◽  
Development Trend ◽  
Internet Technology ◽  
Size Principle ◽  
Network Information ◽  
Teaching Platform ◽  
Multimedia Teaching ◽  
Multimedia Environment ◽  
Use Of Internet

With the rapid development of Internet technology in China, mechanical drawing plays a more and more important role in social development and practice. The main purpose is to use the course of mechanical drawing to accurately represent the appearance, size, principle and technology of the machine. It is often called the language of communication in the industry. In order to make mechanical drawing better applied and practiced, and to make maximum use of Internet technical resources, the establishment of multimedia courseware teaching mechanism is the development trend of mechanical drawing.In order to adapt to the trend of network information, based on the multimedia environment, this paper studies the application of animation technology in mechanical drawing teaching, and constructs a small multimedia teaching platform. Through the explanation of animation courseware, students can better understand the making and principle of mechanical drawing, and cultivate students' divergent thinking. The teaching platform combines teaching material knowledge with practice to deepen students' understanding of the basic content of mechanical drawing. Finally, the rationality and practicability of the system are analyzed by investigating the platform users. The results show that the application of animation technology in mechanical drawing teaching is reasonable and practical.

scholarly journals DESTRUCTIVE OF CURRENT INFORMATION: CONTENT ANALYSIS OF THE HEADLINES OF NEWS AGGREGATORS IN UKRAINE, USA AND RUSSIA

2021 ◽  
Author(s):  
Yaryna Pryshliak ◽  
Keyword(s):  
Quantitative Data ◽  
Rapid Development ◽  
Communication Technologies ◽  
The United States ◽  
Human History ◽  
Media Space ◽  
Negative News ◽  
The Media ◽  
The Impact ◽  
News Headlines

The article outlines the impact of negative news on the minds of recipients, describes the reasons for the audience’s demand for negative information and represents the quantitative data of destructive information in the media space of Ukraine, USA and Russia. The rapid development of communication technologies, which contributes to the creation and dissemination of the largest volumes of information in human history, and therefore negative news, explains the relevance of the chosen topic. The main objectives of the study are news headlines that appear in the feed of the Google News aggregator (regional versions of the United States, Ukraine and Russia).

scholarly journals Integrated computerization of education in the context of the intellectual growth

2021 ◽  
Vol 7 (3A) ◽  
pp. 504-511
Author(s):  
Volodymyr Bekh ◽  
Valerii Akopian ◽  
Sergiy Yashanov ◽  
Ilya Devterov ◽  
Bogdan Kalinichenko
Keyword(s):  
Information And Communication Technologies ◽  
Information Society ◽  
General Trend ◽  
Rapid Development ◽  
Communication Technologies ◽  
Digital Information ◽  
Initial Information ◽  
Information And Communication ◽  
The Individual ◽  
World Development

The rapid development in the world of information and communication technologies makes it possible to say that now they are one of the most common ways of teaching. These technologies influence the formation of methods and methods of pedagogical activity, open up new opportunities for communication and obtaining information. Informatization and computerization of education acts as a component of the general trend of global processes of world development, as an initial information and communication basis for the harmonious development of the individual and social systemic information. Preparing a student for an active and fruitful life in a modern digital information society is one of the main tasks of the modern stage of modernization of the education system.

scholarly journals Semisupervised Community Preserving Network Embedding with Pairwise Constraints

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Dong Liu ◽  
Yan Ru ◽  
Qinpeng Li ◽  
Shibin Wang ◽  
Jianwei Niu
Keyword(s):  
Community Structure ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Nonnegative Matrix ◽  
Machine Learning Algorithms ◽  
Network Visualization ◽  
Network Embedding ◽  
Pairwise Constraints ◽  
Node Clustering ◽  
Low Dimensional

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints (must-link and cannot-link) information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones.

Continual representation learning for evolving biomedical bipartite networks

2021 ◽  
Author(s):  
Kishlay Jha ◽  
Guangxu Xun ◽  
Aidong Zhang
Keyword(s):  
Network Structure ◽  
Learning Strategy ◽  
Structure Learning ◽  
Fundamental Problem ◽  
Representation Learning ◽  
Research Area ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Straightforward Application ◽  
Low Dimensional

Abstract Motivation Many real-world biomedical interactions such as ‘gene-disease’, ‘disease-symptom’ and ‘drug-target’ are modeled as a bipartite network structure. Learning meaningful representations for such networks is a fundamental problem in the research area of Network Representation Learning (NRL). NRL approaches aim to translate the network structure into low-dimensional vector representations that are useful to a variety of biomedical applications. Despite significant advances, the existing approaches still have certain limitations. First, a majority of these approaches do not model the unique topological properties of bipartite networks. Consequently, their straightforward application to the bipartite graphs yields unsatisfactory results. Second, the existing approaches typically learn representations from static networks. This is limiting for the biomedical bipartite networks that evolve at a rapid pace, and thus necessitate the development of approaches that can update the representations in an online fashion. Results In this research, we propose a novel representation learning approach that accurately preserves the intricate bipartite structure, and efficiently updates the node representations. Specifically, we design a customized autoencoder that captures the proximity relationship between nodes participating in the bipartite bicliques (2 × 2 sub-graph), while preserving both the global and local structures. Moreover, the proposed structure-preserving technique is carefully interleaved with the central tenets of continual machine learning to design an incremental learning strategy that updates the node representations in an online manner. Taken together, the proposed approach produces meaningful representations with high fidelity and computational efficiency. Extensive experiments conducted on several biomedical bipartite networks validate the effectiveness and rationality of the proposed approach.

Progress and Prospects for Estonian Libraries

2011 ◽  
pp. 221-237 ◽  
Author(s):  
Sirje Virkus
Keyword(s):  
Information And Communication Technologies ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Rapid Development ◽  
Communication Technologies ◽  
Library Services ◽  
The Past ◽  
Challenges And Opportunities ◽  
Information And Communication ◽  
New Challenges

The rapid development of information and communication technology (ICT) over the past decades has created new challenges and opportunities for libraries and librarians. As a result of ICT, library services to users have changed, the management of libraries has evolved and the roles of librarians have multiplied. The new millennium presents new opportunities to exploit an ever-growing array of information and communication technologies in the provision of library services. As one millennium draws to a close and a new one begins, there are a lot of questions to answer:

scholarly journals A Survey on True Random Number Generators Based on Chaos

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Yu ◽  
Lixiang Li ◽  
Qiang Tang ◽  
Shuo Cai ◽  
Yun Song ◽  
...  
Keyword(s):  
Chaotic System ◽  
Key Management ◽  
Random Number ◽  
Initial Conditions ◽  
Rapid Development ◽  
Current Mode ◽  
Random Numbers ◽  
Random Number Generators ◽  
Network Information ◽  
Survey Paper

With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

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