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23rd International Conference on Artificial Intelligence in Education (AIED)

作者： Al-Zawqari, Ali Vandersteen, Gerd Vrije Univ Brussel Dept ELEC B-1050 Brussels Belgium

ISBN: (纸本)9783031116476;9783031116469

The goal of this research is to strengthen the teaching strategy with quantitatively measured learning analytics. The entropy-based learning analytics aims to measure and understand students' progress by quantitatively measuring the difference between the content to be learned, the tutors' expectation of understanding, and the student's knowledge. This quantification will take similar steps than taken by Shannon for his information theory using a mathematical formalism to quantitatively measure knowledge (equivalent to Shannon's entropy) and knowledge transfer (equivalent to Shannon's mutual information). Knowledge graphs will be used to represent the content to be learned, the tutors' expectations, and the student's knowledge. Early results reveal that advanced analytical algorithms and graph entropy specified for educational applications is necessary for this research project to succeed.

关键词： Probabilistic graphical models Shannon theory graph entropy Learning analytics

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An efficient strategy for identifying cancer-related key genes based on graph entropy

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2018年 74卷 142-148页

作者： Zhang, Wei Wang, Shu-Lin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

Gene networks are beneficial to identify functional genes that are highly relevant to clinical outcomes. Most of the current methods require information about the interaction of genes or proteins to construct genetic network connection. However, the conclusion of these methods may be bias because of the current incompleteness of human interactome. In this paper, we propose an efficient strategy to use gene expression data and gene mutation data for identifying cancer-related key genes based on graph entropy (iKGGE). Firstly, we construct a gene network using only gene expression data based on the sparse inverse covariance matrix, then, cluster genes use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs, and at last, we introduce a novel metric that combine graph entropy and the influence of upstream gene mutations information to measure the impact factors of genes. Testing of the three available cancer datasets shows that our strategy can effectively extract key genes that may play distinct roles in tumorigenesis, and the cancer patient risk groups are well predicted based on key genes. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Cancer-related key genes Gene network Parallel maximal cliques graph entropy

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RWE: A Random Walk Based graph entropy for the Structural Complexity of Directed Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 2264-2278页

作者： Zhang, Chong Deng, Cheng Fu, Luoyi Wang, Xinbing Chen, Guihai Zhou, Lei Zhou, Chenghu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Oceanog Shanghai 200240 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100045 Peoples R China

This paper studies a graph entropy measure to characterize the structural complexity of directed networks. Since the von Neumann entropy (VNE) has found applications in many tasks by networked data, but suffers from a high computational complexity in computing the Laplacian spectrum, we aim to propose a simple yet effective alternative method. Considering that local nodal interactions collectively induce the information flow of the entire graph, we are motivated to use the probability flow of random walks as the proxy of information flow in real-world digraphs, and correspondingly propose a random walk based entropy (RWE) to measure the average information that the random walker reaches each node. Inspired by the close relation between Laplacian spectrum and the Perron vector, we prove that RWE serves as a good approximation for the VNE in digraphs with a guaranteed entropy gap. This approximation is further applied to a digraph similarity measure based on the Jensen-Shannon divergence. Therefore, RWE exhibits interpretability, scalability and capability of well capturing the structural complexity of digraphs as empirically verified. We further extend RWE to two dimensions by incorporating community structures, which characterizes information flow both between and within communities. By proving that the difference between the one-dimensional and two-dimensional RWE reflects the extent to which the community structure is preserved, we convert the community detection problem into the minimization of two-dimensional RWE, and design a greedy algorithm. Various experiments confirm the superiority of our approach over the baselines.

关键词： entropy Complexity theory Laplace equations Directed graphs Receivers Social networking (online) Extraterrestrial measurements Directed graph graph entropy random walk structural complexity von Neumann entropy

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Distributed Computing and the graph entropy Region

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IEEE TRANSACTIONS ON INFORMATION THEORY 2014年第6期60卷 3435-3449页

作者： Shayevitz, Ofer Tel Aviv Univ Dept Elect Engn Syst IL-6997801 Tel Aviv Israel

Two remote senders observe X and Y, respectively, and can noiselessly send information via a common relay node to a receiver that observes Z. The receiver wants to compute a function f(X, Y, Z) of these possibly related observations, without error. We study the average number of bits that need to be conveyed to that end by each sender to the relay and by the relay to the receiver, in the limit of multiple instances. We relate these quantities to the entropy region of a probabilistic graph with respect to a Cartesian representation of its vertex set, which we define as a natural extension of graph entropy. General properties and bounds for the graph entropy region are derived, and mapped back to special cases of the distributed computing setup.

关键词： Distributed source coding zero-error information theory graph entropy

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Information processing in complex networks: graph entropy and information functionals

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APPLIED MATHEMATICS AND COMPUTATION 2008年第1-2期201卷 82-94页

作者： Dehmer, Matthias Vienna Univ Technol Inst Discrete Math & Geometry A-1040 Vienna Austria

This paper introduces a general framework for de. ning the entropy of a graph. Our definition is based on a local information graph and on information functionals derived from the topological structure of a given graph. More precisely, an information functional quantifies structural information of a graph based on a derived probability distribution. Such a probability distribution leads directly to an entropy of a graph. Then, the structural information content of a graph will be is interpreted and defined as the derived graph entropy. Another major contribution of this paper is the investigation of relationships between graph entropies. In addition to this, we provide numerical results demonstrating not only the feasibility of our method, which has polynomial time complexity, but also its usefulness with regard to practical applications aiming to an understanding of information processing in complex networks. (C) 2007 Elsevier Inc. All rights reserved.

关键词： graphs structural information content graph entropy information theory applied graph theory

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Multi-stage dynamic disinformation detection with graph entropy guidance

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2024年第2期27卷 1-21页

作者： Hao, Xiaorong Liu, Bo Yang, Xinyan Sun, Xiangguo Meng, Qing Cao, Jiuxin Southeast Univ Nanjing Peoples R China Purple Mt Labs Nanjing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Hohai Univ Nanjing Peoples R China Sanjiang Univ Nanjing Peoples R China

Online disinformation has become one of the most severe concerns in today's world. Recognizing disinformation timely and effectively is very hard, because the propagation process of disinformation is dynamic and complicated. The existing newest research leverage uniform time intervals to study the multi-stage propagation features of disinformation. However, uniform time intervals are unrealistic in the real world, cause the process of information propagation is not regular. In light of these facts, we propose a novel and effective framework Multi-stageDynamicDisinformationDetection with graph entropy Guidance(MsDD) to better analyze multi-stage propagation patterns. Instead of traditional snapshots, we analyze the dynamic propagation network via graph entropy, which can work effectively in finding the dynamic and variable-length stages. In this way, we can explicitly learn the changing pattern of propagation stages and support timely detection even at the early stages. Based on this effective multi-stage analysis framework, we further propose a novel dynamic analysis model to model both the structural and sequential evolving features. Extensive experiments on two real-world datasets prove the superiority of our model. We open the datasets and source code at https://***/researchxr/MsDD.

关键词： Disinformation detection Multi-stage learning graph entropy Dynamic analysis

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graph entropy-based clustering algorithm in medical brain image database 11

Graph entropy-based clustering algorithm in medical brain im...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhan, Yu Pan, Haiwei Xie, Xiaoqin Zhang, Zhiqiang Li, Wenbo Harbin Engn Univ Coll Comp Sci & Technol 21Bldg145 Nantong St Harbin 150001 Heilongjiang Peoples R China

The high incidence of brain tumor has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors' diagnosis. Clustering medical images for Intelligent Decision Support is an important part in the field of medical image mining because there are several technical aspects which make this problem challenging. In this paper, we propose a medical brain image clustering method to find similar pathology images that can assist doctors to analyze the specific disease, discover its potential cause and make more accurate treatment. Firstly, this method represents medical brain image dataset as a weighted, undirected and complete graph. Secondly, this graph is sparsified so as to describe the similarity of medical images very well. Last but not the least, a graph entropy based clustering method for this sparsified graph is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity. The clustering results can better describe the similarity of medical images.

关键词： Medical image graph entropy sparsification clustering

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graph entropy from Closed Walk and Cycle Functionals

Graph Entropy from Closed Walk and Cycle Functionals

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Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR) and Statistical Techniques in Pattern Recognition (SPR)

作者： Aziz, Furqan Hancock, Edwin R. Wilson, Richard C. IM Sci Dept Comp Sci Peshawar Pakistan Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9783319490557;9783319490540

This paper presents an informational functional that can be used to characterise the entropy of a graph or network structure, using closed random walks and cycles. The work commences from Dehmer's information functional, that characterises networks at the vertex level, and extends this to structures which capture the correlation of vertices, using walk and cycle structures. The resulting entropies are applied to synthetic networks and to network time series. Here they prove effective in discriminating between different types of network structure, and detecting changes in the structure of networks with time.

关键词： graph entropy Random walks Ihara coefficients

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SERGE: Successive Event Recommendation Based on graph entropy for Event-Based Social Networks

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IEEE ACCESS 2018年 6卷 3020-3030页

作者： Liu, Shenghao Wang, Bang Xu, Minghua Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Journalism & Informat Commun Wuhan 430074 Hubei Peoples R China

With the fast development of many event-based social networks (EBSNs), event recommendation, which is to recommend a list of upcoming events to a user according to his preference, has attracted a lot of attentions in both academia and industry. In this paper, we propose a successive event recommendation based on graph entropy (SERGE) to deal with the new event cold start problem by exploiting diverse relations as well as asynchronous feedbacks in EBSNs. The SERGE creates recommendation lists at discrete times during each publication period. At the beginning, it constructs a primary graph (PG) based on the entities and their relations in an EBSN and computes the user-event similarity scores by applying a random walk with restart (RWR) algorithm on PG. At each recommendation time, it then constructs a feedback graph (FG) based on the up-to-date user feedbacks on event reservations and applies the RWR again on FG to compute new user-event similarity scores. We then propose to weight the two sets of similarity scores with the graph entropies of both PG and FG and create the final recommendation lists accordingly. We have crawled two datasets from a real EBSN for two cities, Beijing and Shanghai in China. Experimental results validate the effectiveness and superiority of the proposed SERGE scheme over the peer schemes.

关键词： Successive event recommendation random walk with restart graph entropy cold start problem event-based social networks

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Conditional graph entropy as an Alternating Minimization Problem

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第2期70卷 904-919页

作者： Harangi, Viktor Niu, Xueyan Bai, Bo Alfred Renyi Inst Math H-1053 Budapest Hungary Huawei Technol Co Ltd Cent Res Inst Theory Lab Labs 2012 Hong Kong Peoples R China

Conditional graph entropy is known to be the minimal rate for a natural functional compression problem with side information at the receiver. In this paper we show that it can be formulated as an alternating minimization problem, which gives rise to a simple iterative algorithm for numerically computing (conditional) graph entropy. This also leads to a new formula which shows that conditional graph entropy is part of a more general framework: the solution of an optimization problem over a convex corner. In the special case of graph entropy (i.e., unconditioned version) this was known due to Csiszar, Korner, Lovasz, Marton, and Simonyi. In that case the role of the convex corner was played by the so-called vertex packing polytope. In the conditional version it is a more intricate convex body but the function to minimize is the same. Furthermore, we describe a dual problem that leads to an optimality check and an error bound for the iterative algorithm.

关键词： graph entropy functional compression independent sets vertex packing polytope alternating optimization

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