My research mainly focuses on Data Mining, Machine Learning, sepecially on Deep Learning and network analysis.
Can supervise: YES
Guo, T, Pan, S, Zhu, X & Zhang, C 2019, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering.View/Download from: UTS OPUS or Publisher's site
IEEE Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, yet they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Guo, T, Wu, J, Zhu, X & Zhang, C 2017, 'Combining structured node content and topology information for networked graph clustering', ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3.View/Download from: UTS OPUS or Publisher's site
© 2017 ACM. Graphs are popularly used to represent objects with shared dependency relationships. To date, all existing graph clustering algorithms consider each node as a single attribute or a set of independent attributes, without realizing that content inside each node may also have complex structures. In this article, we formulate a new networked graph clustering task where a network contains a set of inter-connected (or networked) super-nodes, each of which is a single-attribute graph. The new super-node representation is applicable to many real-world applications, such as a citation network where each node denotes a paper whose content can be described as a graph, and citation relationships between papers form a networked graph (i.e., a supergraph). Networked graph clustering aims to find similar node groups, each of which contains nodes with similar content and structure information. The main challenge is to properly calculate the similarity between super-nodes for clustering. To solve the problem, we propose to characterize node similarity by integrating structure and content information of each super-node. To measure node content similarity, we use cosine distance by considering overlapped attributes between two super-nodes. To measure structure similarity, we propose an Attributed Random Walk Kernel (ARWK) to calculate the similarity between super-nodes. Detailed node content analysis is also included to build relationships between super-nodes with shared internal structure information, so the structure similarity can be calculated in a precise way. By integrating the structure similarity and content similarity as one matrix, the spectral clustering is used to achieve networked graph clustering. Our method enjoys sound theoretical properties, including bounded similarities and better structure similarity assessment than traditional graph clustering methods. Experiments on real-world applications demonstrate that our method significantly outperforms basel...
Li, B, Guo, T, Zhu, X & Li, Z 2015, 'Reverse twin plant for efficient diagnosability testing and optimizing', Engineering Applications of Artificial Intelligence, vol. 38, pp. 131-137.View/Download from: Publisher's site
© 2014 Elsevier Ltd. All rights reserved. Model-based diagnosis in discrete event systems (DESs) is a major research topic in failure diagnosis, where diagnosability plays an important role in the construction of the diagnosis engine. To improve the solution efficiency for diagnosability, this paper proposes novel techniques to solve the problems of testing and optimizing for diagnosability. We propose a new concept, reverse twin plant, which is generated backwards from the final states of the DESs so there is no need to generate a complete copy of the DES model to determine the diagnosability. Such a design makes our testing algorithm much faster than existing methods. An efficient optimizing algorithm, which makes a non-diagnosable system diagnosable, is also proposed in the paper by expanding the minimal observable space with operation on just a part of the DES model. Examples and theoretical studies demonstrate the performance of the proposed designs.
Zhang, B, Zhang, L, Guo, T, Wang, Y & Chen, F 2018, 'Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process Model', KDD'18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM SIGKDD International Conference on Knowledge Discovery and Data Minin), Association for Computing Machinery, London, ENGLAND, pp. 2692-2700.View/Download from: UTS OPUS or Publisher's site
© 2016 NIPS Foundation - All Rights Reserved. The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e.g., hidden Markov model) and stochastic interaction point process models (e.g., Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing the hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and automatically determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Many natural and social phenomena can be modeled by interaction point processes (IPPs) (Diggle et al. 1994), stochastic point processes considering the interaction between points. In this paper, we propose the infinite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e.g., Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). It treats IPP as a mixture of basis point processes with the aid of a distance dependent prior over branching structure that describes the relationship between points. The IBM can estimate point event intensity, interaction mechanism and branching structure simultaneously. A generic Metropolis-within-Gibbs sampling method is also developed for model parameter inference. The experiments on synthetic and real-world data demonstrate the superiority of the IBM.
Guo, T, Zhu, XQ, Pei, J & Zhang, C 2014, 'SNOC: Streaming Network Node Classification', Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining, IEEE, Shenzhen, PEOPLES R CHINA, pp. 150-159.View/Download from: UTS OPUS or Publisher's site
Many real-world networks are featured with dynamic changes, such as new nodes and edges, and modification of the node content. Because changes are continuously introduced to the network in a streaming fashion, we refer to such dynamic networks as streaming networks. In this paper, we propose a new classification method for streaming networks, namely streaming network node classification (SNOC). For streaming networks, the essential challenge is to properly capture the dynamic changes of the node content and node interactions to support node classification. While streaming networks are dynamically evolving, for a short temporal period, a subset of salient features are essentially tied to the network content and structures, and therefore can be used to characterize the network for classification. To achieve this goal, we propose to carry out streaming network feature selection (SNF) from the network, and use selected features as gauge to classify unlabeled nodes. A Laplacian based quality criterion is proposed to guide the node classification, where the Laplacian matrix is generated based on node labels and structures. Node classification is achieved by finding the class that results in the minimal gauging value with respect to the selected features. By frequently updating the features selected from the network, node classification can quickly adapt to the changes in the network for maximal performance gain. Experiments demonstrate that SNOC is able to capture changes in network structures and node content, and outperforms baseline approaches with significant performance gain.
Guo, T & Zhu, X 2014, 'Super-Graph Classification', Proceeding of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, The 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Tainan, Taiwan, pp. 323-336.View/Download from: Publisher's site
Graphs are popularly used to represent objects with dependency structures, yet all existing graph classification algorithms can only handle simple graphs where each node is a single attribute (or a set of independent attributes). In this paper, we formulate a new super-graph classification task where each node of the super-graph may contain a graph (a single-attribute graph), so a super-graph contains a set of inter-connected graphs. To support super-graph classification, we propose a Weighted Random Walk Kernel (WRWK) which generates a product graph between any two super-graphs, and uses the similarity (kernel value) of two single-attribute graph as the node weight. Then we calculate weighted random walks on the product graph to generate kernel value between two super-graphs as their similarity. Our method enjoys sound theoretical properties, including bounded similarity. Experiments confirm that our method significantly outperforms baseline approaches.
Guo, T, Chi, L & Zhu, X 2013, 'Graph Hashing and Factorization for Fast Graph Stream Classification', Proceedings of the 22nd ACM international conference on Conference on information & knowledge management CIKM, ACM international conference on Conference on information & knowledge management, ACM, San Francisco, CA, USA, pp. 1607-1612.View/Download from: UTS OPUS or Publisher's site
Graph stream classification concerns building learning models from continuously growing graph data, in which an essential step is to explore subgraph features to represent graphs for effective learning and classification. When representing a graph using subgraph features, all existing methods employ coarse-grained feature representation, which only considers whether or not a subgraph feature appears in the graph. In this paper, we propose a fine-grained graph factorization approach for Fast Graph Stream Classification (FGSC). Our main idea is to find a set of cliques as feature base to represent each graph as a linear combination of the base cliques. To achieve this goal, we decompose each graph into a number of cliques and select discriminative cliques to generate a transfer matrix called Clique Set Matrix (M). By using M as the base for formulating graph factorization, each graph is represented in a vector space with each element denoting the degree of the corresponding subgraph feature related to the graph, so existing supervised learning algorithms can be applied to derive learning models for graph classification.
Guo, T & Zhu, X 2013, 'Understanding the Roles of Sub-graph Features for Graph Classification: An Empirical Study Perspective', Proceedings of the 22nd ACM international conference on Conference on information & knowledge manage CIKM, ACM international conference on Conference on information & knowledge manage CIKM, ACM, San Francisco, CA, USA, pp. 817-822.View/Download from: UTS OPUS or Publisher's site
Graph classification concerns the learning of discriminative models, from structured training data, to classify previously unseen graph samples into specific categories, where the main challenge is to explore structural information in the training data to build classifiers. One of the most common graph classification approaches is to use sub-graph features to convert graphs into instance-feature representations, so generic learning algorithms can be applied to derive learning models. Finding good sub-graph features is regarded as an important task for this type of learning approaches, despite that there is no comprehensive understanding on (1) how effective sub-graph features can be used for graph classification? (2) how many sub-graph features are sufficient for good classification results? (3) does the length of the sub-graph features play major roles for classification? and (4) whether some random sub-graphs can be used for graph representation and classification?