Rcanalyzer: Visual Analytics of Rare Categories in Dynamic Networks∗
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Pan at al. / Front Inform Technol Electron Eng 2018 19(1):1-5 1 Frontiers of Information Technology & Electronic Engineering www.jzus.zju.edu.cn; engineering.cae.cn; www.springerlink.com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: [email protected] RCAnalyzer: Visual Analytics of Rare Categories in Dynamic Networks∗ Jiacheng Pany1, Dongming Han1, Fangzhou Guo1, Dawei Zhou2, Nan Cao3, Jingrui He2, Mingliang Xu4 5, Wei Chenyz1 1State Key Lab of CAD & CG, Zhejiang University, Hangzhou, Zhejiang, P.R. China, 310058 2Department of Computer Science and Engineering, Arizona State University, Arizona 3Tong Ji Intelligent Big Data Visualisation Lab (iDVx Lab), TongJi University, Shanghai, P.R. China 4The School of Information Engineering, Zhengzhou University, Zhengzhou, P.R. China, 450000 5Henan Institute of Advanced Technology, Zhengzhou University yE-mail: [email protected]; [email protected] Received mmm. dd, 2016; Revision accepted mmm. dd, 2016; Crosschecked mmm. dd, 2017 Abstract: A dynamic network refers to a graph structure whose nodes and/or links will dynamically change over time. Existing visualization and analysis techniques mainly focus on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in a dynamic network, the rare occurrence of which could damage the development of the entire structure. In this paper, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verified the effectiveness of our system. Key words: Rare Category Detection; Dynamic Networks; Visual Analytics https://doi.org/10.1631/FITEE.1000000 CLC number: TP 1 Introduction a network behave normally, while a minority may act differently from the others, indicating anoma- In many cases, relations among objects can be lous situations. Anomalies could be positive, such modeled as time-evolving networks, such as the col- as superstars in a collaboration network and recipi- laborations among researchers, transactions among ents or benefactors in a financial network, or nega- traders, and communications in social networks. tive enough to damage the development of the entire These relations reflect how individuals act in a net- graph, such as frauds in a trading network and crim- work over time and reflect the goals of their activ- inals or spies in a communication network. In either ities (Jovanovic et al., 2015). Most individuals in case, finding these anomalous changing behaviors of z Corresponding author network structures is valuable. * This research is supported by National Natural Science Foun- dation of China (U1866602,61772456) Most of the existing anomaly detection algo- ORCID: Jia-cheng PAN, https://orcid.org/0000-0002-8676- rithms are automatic, and do not take human in- 9990 c Zhejiang University and Springer-Verlag GmbH Germany, part sights into account. In contrast, active learning is a of Springer Nature 2018 special case of machine learning that improves auto- 2 Pan at al. / Front Inform Technol Electron Eng 2018 19(1):1-5 matic algorithms’ performance with human knowl- gregates the graph structure into a hierarchy so that edge. Following an active learning procedure, many a large graph can be fully displayed while showing rare category detection (RCD) methods are thus de- the detailed structures of potential rare categories. veloped following (He and Carbonell, 2009, 2008; The proposed matrix based visualization facilitates Huang et al., 2013, 2011; Pelleg and Moore, 2005), an in-context visual comparison of substructures in i.e., candidates that are most likely to represent rare a dynamic graph, thus helping with rare category categories are detected and shown to be labeled by detection. In particular, this paper has the following users. Rare category detection methods are one set contributions: of anomaly detection algorithms which recognize ab- normal individuals as rare categories because their number is usually very small. Once labeled, the al- gorithm will propagate the label to the nearby in- stances which are similar to the labeled one in a feature space. Those representative candidates are usually centers of rare categories. This procedure has one major limitation, i.e., it is still difficult for users Dendrogram to make a correct judgment (i.e., whether or not the candidate represents a rare category) by only show- ing one single data instance to them with the entire context information missing. This is particularly dif- ficult for detecting rare categories from a dynamic graph as both the temporal and structural informa- Matrix Sankey Diagram tion need to be considered while labeling a candidate. Fig. 1 The basic design of the matrices view is a Therefore, visualization could be helpful in terms of combination of matrix, Sankey diagram and dendro- supporting the interactive data exploration and pro- gram. Compared to a square matrix, triangles are viding a rich context representation. more space efficient. However, challenges exist in designing such a visualization system to support the process of rare category detection in a dynamic network. First, al- • A novel tree cut algorithm that produces a though capturing the temporal dynamics of a chang- multi-focus view to illustrate the substructure ing structure itself is a problem that has been exten- details of multiple rare categories in the context sively studied (Beck et al., 2014), none of the existing of a big dynamic graph. techniques is developed to support the visualization • A novel dynamic network visualization design of rare categories. Second, capturing the changing in the form of a series of connected triangular structures of rare categories in the context of a big matrices that highlights the detected rare cate- dynamic graph is challenging as the rare categories gories in both the temporal and topological con- are usually very small and their evolutions could be text, facilitating the substructure comparison. very likely to be ignored. Third, to better support the decision-making process, the visualization should • An integrated visual analysis system that sup- be able to differentiate different structures in detail, ports the detection of rare categories and facili- and this is not easy to achieve. tates rare category labeling. To address the above challenges, in this paper, we propose a novel visualization system called RC- The paper is organized as follows. Related work Analyzer. RCAnalyzer represents a large dynamic is discussed in section 2. The BIRD algorithm and network in the form of a series of connected tri- analytical tasks are introduced in section 3. In sec- angular matrices with each matrix representing a tion 4 we introduce the design of our system. System snapshot (Fig. 1). A hierarchical clustering algo- evaluations are introduced in section 5. We discuss rithm and a tree cut algorithm are developed to our work in section 6 and conclude the paper in sec- produce an adaptive focus+context view that ag- tion 7. Pan at al. / Front Inform Technol Electron Eng 2018 19(1):1-5 3 2 Related work in dynamic networks. 2.1 Dynamic network anomaly detection 2.2 Visualization of anomaly Anomaly detection in dynamic networks refers Many visualization techniques have been devel- to the detection of anomalous nodes, edges, sub- oped to help the detection and analysis of anomalies graphs, and time-evolving changes. Several existing (Haberkorn et al., 2014; Liu et al., 2017; Chandola surveys have reviewed the most popular anomaly de- et al., 2009; Zhang et al., 2017). Dimension reduc- tection methods used in dynamic networks (Bhuyan tion methods, such as principal component analy- et al., 2013; Ranshous et al., 2015). Ranshous sis(PCA) (Jolliffe and Ian, 1986), and multidimen- et al. categorized the existing methods into 5 sional visualization techniques, such as parallel co- types (Ranshous et al., 2015): community-based, ordinate plots (Inselberg, 2009) and DICON (Cao compression-based, decomposition-based, distance- et al., 2011), are commonly used to visualize the based, and probabilistic-model-based. For exam- data distribution and show outliers with abnormal ple, based on compression based methods, a graph distribution. In ViDX (Xu et al., 2017), an extended stream can be divided into multiple segmentations Marey’s graph is used to show outliers in the man- using the minimum description length (MDL) prin- ufacturing procedure. Anomalies in network traffic ciple. Anomaly changes can be then detected at the data (Corchado and Herrero, 2011; Tsai et al., 2009; time points when a new segment begins (Sun et al., Teoh et al., 2002) and social media data (Thom et al., 2007). Probabilistic-model-based methods usually 2012; Zhao et al., 2014; Cao et al., 2016) have also construct a "normal" model and use it to detect drawn a lot of attention. Fluxflow (Zhao et al., 2014) anomalies that deviate from the "normal" model. detects the diffusion of anomalous information in so- For example, when the number of communications cial media and TargetVue (Cao et al., 2016) uses deviates from the expected number generated by glyph-based designs to show the anomalous behav- conjugate Bayesian models, the time point would be iors in online communication systems based on an considered as an anomaly (Heard et al., 2010). unsupervised learning model. Wang et al. (Wang As we mentioned in Section 1, these anomaly et al., 2013) presented SentiView to visualize the detection works do not capture user’s intention.