The networked-loan is major financing support for Micro, Small and Medium-sized Enterprises (MSMEs) in some developing countries. But external shocks may weaken the financial networks' robustness; an accidental default may spread across the network and collapse the whole network. Thus, predicting the critical firms in networked-loans to stem contagion risk and prevent potential systemic financial crises is of crucial significance to the long-term health of inclusive finance and sustainable economic development. Existing approaches in the banking industry dismiss the contagion risk across loan networks and need extensive knowledge with sophisticated financial expertise. Regarding the issues, we propose a novel approach to predict critical firms for stemming contagion risk in the bank industry with deep reinforcement learning integrated with high-order graph message-passing networks. We demonstrate that our approach outperforms the state-of-the-art baselines significantly on the dataset from a large commercial bank. Moreover, we also conducted empirical studies on the real-world loan dataset for risk mitigation. The proposed approach enables financial regulators and risk managers to better track and understands contagion and systemic risk in networked-loans. The superior performance also represents a paradigm shift in addressing the modern challenges in financing support of MSMEs and sustainable economic development.

In networked-loans, guarantor enterprises have a legal duty to repay debt to the commercial bank when the guaranteed borrower enterprise defaults (fail to repay). During an economic recession, the risk of defaults may spread like wildfire -- the loan network structure could amplify both reach and impact; thus leading to a large-scale corporation defaults even systemic financial crises. The Central Bank urges advanced regulation technology to recognize and act on the contagion risk in order to avoid the ‘`gray rhino’'. Therefore, we present a novel approach to help the regulators quantify the systemic risk and provide stemming clues. In particular, we report a state-of-the-art graph neural network architecture (iConReg) for detecting and isolating of contagion risk in China's national-wide networked-loans. The overall accuracy of our model reaches over 91% of AUC, which considerably outperforms the compared benchmark methods. By isolating the top 1% of predicted high-risky nodes in the contagion chains, iConReg reports a significant shrink (averaged 25.8%) of loan default rates. Our presented approach opens up a new direction of using deep graph learning techniques to regulate the contagion risk of networked-loans, which enables the authorities to design more prompt prevention measures against systemic financial crises

Under debt obligation promises, small- and medium-sized enterprises (SMEs) can guarantee each other to enhance their financial security to get loans from commercial banks. When the economy rises, the banks may reduce the threshold to some extent, which may introduce default risk during the economy down period, especially when many SMEs bind together and form complex networks. The risk may diffuse across the guarantee network and may result in a financial crisis. Macroprudential oversight of the guarantee network to eliminate any potential systematics financial risk is the central task of the regulatory commission and the commercial banks. Based on our observation, the delinquent probability of an SME depends not only on self-financial status but also highly related to its temporal behaviors and structural position in networks. The classic approach for loan assessment criteria face challenges in extracting temporal and structural patterns from dynamic networks. To address these issues, we propose a temporal delinquent event prediction (TDEP) framework that preserves temporal network structures and credit behavior sequences in an end-to-end model. In particular, we first employ a graph attention layer to learn the representation of nodes in temporal guarantee networks. We then design a recursive and self-attention mechanism to integrate both credit behavior and network structure information. The learned attentional weights are leveraged to uncover high-risk guarantee patterns that effectively accelerate the risk assessment process. Afterward, we conduct extensive experiments in a real-world guaranteed-loan data set to evaluate its performance. The results show the effectiveness of our proposed approach compared with the state-of-the-art baselines. Finally, we integrate the proposed model in a real-world loan risk management system. We present the implementation details of each subcomponent of the system and report out the performance after online deployment.

Identifying protein subcellular locations is an important topic in protein function prediction. Interacting proteins may share similar locations. Thus, it is imperative to infer protein subcellular locations by taking protein-protein interactions (PPIs)into account. In this study, we present a network embedding-based method, node2loc, to identify protein subcellular locations. node2loc first learns distributed embeddings of proteins in a protein-protein interaction (PPI)network using node2vec. Then the learned embeddings are further fed into a recurrent neural network (RNN). To resolve the severe class imbalance of different subcellular locations, Synthetic Minority Over-sampling Technique (SMOTE)is applied to artificially synthesize proteins for minority classes. node2loc is evaluated on our constructed human benchmark dataset with 16 subcellular locations and yields a Matthews correlation coefficient (MCC)value of 0.800, which is superior to baseline methods. In addition, node2loc yields a better performance on a Yeast benchmark dataset with 17 locations. The results demonstrate that the learned representations from a PPI network have certain discriminative ability for classifying protein subcellular locations. However, node2loc is a transductive method, it only works for proteins connected in a PPI network, and it needs to be retrained for new proteins. In addition, the PPI network needs be annotated to some extent with location information. node2loc is freely available at https://github.com/xypan1232/node2loc.

In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. In contrast to traditional statistical and machine learning methods, the visual analysis based tool focuses on analytical thinking, user interactions and answering business questions by examining different visual analysis views. In the demonstration, conference attendees will interact with VAP and learn its capabilities in discovering typical consumption patterns and spatio-temporal shift patterns from a real-world case study of electricity.

The small and medium-sized enterprises (SMEs) are allowed to guarantee each other and form complex loan networks to receive loans from banks during the economic expansion stage. However, external shocks may weaken the robustness, and an accidental default may spread across the network and lead to large-scale defaults, even systemic crisis. Thus, predicting and rating the default contagion chains in the guarantee network in order to reduce or prevent potential systemic financial risk, attracts a grave concern from the Regulatory Authority and the banks. Existing credit risk models in the banking industry utilize machine learning methods to generate a credit score for each customer. Such approaches dismiss the contagion risk from guarantee chains and need extensive feature engineering with deep domain expertise. To this end, we propose a novel approach to rate the risk of contagion chains in the bank industry with the deep neural network. We employed the temporal inter-chain attention network on graph-structured loan behavior data to compute risk scores for the contagion chains. We show that our approach is significantly better than the state-of-the-art baselines on the dataset from a major financial institution in Asia. Besides, we conducted empirical studies on the real-world loan dataset for risk assessment. The proposed approach enabled loan managers to monitor risks in a boarder view and avoid significant financial losses for the financial institution.

Commercial banks normally require Small and Medium Enterprises (SMEs) to provide their warranties when applying for a loan. If the borrower defaults, the guarantor is obligated to repay its loan. Such a guarantee system is designed to reduce delinquent risks, but may introduce a new dimension risk if more and more SMEs involve and subsequently form complex temporal networks. Monitoring the financial status of SMEs in these networks, and preventing or reducing systematic loan risk, is an area of great concern for both the regulatory commission and the banks. To allow possible actions to be taken in advance, this paper studies the problem of predicting repayment delinquency in the networked-guarantee loans. We propose a dynamic default prediction framework (DDPF), which preserves temporal network structures and loan behavior sequences in an end-to-end model. In particular, we design a gated recursive and attention mechanism to integrate both the loan behavior and network information. Then, we uncover risky warrant patterns by the learned weights, which effectively accelerate risk evaluation process. Finally, we conduct extensive experiments in a real-world loan risk control system to evaluate its performance, the results demonstrate the effectiveness of our proposed approach compared with state-of-the-art baselines.

Assessing and predicting the default risk of networked-guarantee loans is critical for the commercial banks and financial regulatory authorities. The guarantee relationships between the loan companies are usually modeled as directed networks. Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for the assessment of systematic financial risk level. In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks. Because this financial network is different from other complex networks, such as social, language, or citation networks, we set the binary roles of vertices and define high-order adjacent measures based on financial domain characteristics. We design objective functions in addition to a graph attention layer to capture the importance of nodes. We implement a productive learning strategy and prove that the complexity is near-linear with the number of edges, which could scale to large datasets. Extensive experiments demonstrate the superiority of our model over state-of-the-art method. We also evaluate the model in a real-world loan risk control system, and the results validate the effectiveness of our proposed approaches.

Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors.

Groups of enterprises can guarantee each other and form complex networks in order to try to obtain loans from banks. Monitoring the financial status of a network, and preventing or reducing systematic risk in case of a crisis, is an area of great concern for the regulatory commission and for the banks. We set the ultimate goal of developing a visual analytic approach and tool for risk dissolving and decision-making. We have consolidated four main analysis tasks conducted by financial experts: i) Multi-faceted Default Risk Visualization, whereby a hybrid representation is devised to predict the default risk and an interface developed to visualize key indicators; ii) Risk Guarantee Patterns Discovery. We follow the Shneiderman mantra guidance for designing interactive visualization applications, whereby an interactive risk guarantee community detection and a motif detection based risk guarantee pattern discovery approach are described; iii) Network Evolution and Retrospective, whereby animation is used to help users to understand the guarantee dynamic; iv) Risk Communication Analysis. The temporal diffusion path analysis can be useful for the government and banks to monitor the spread of the default status. It also provides insight for taking precautionary measures to prevent and dissolve systematic financial risk. We implement the system with case studies using real-world bank loan data. Two financial experts are consulted to endorse the developed tool. To the best of our knowledge, this is the first visual analytics tool developed to explore networked-guarantee loan risks in a systematic manner.

Portfolio risk control is vital to financial institutions: investors seek to build equities with the highest return but with minimum risk. However, a general phenomenon is significant comovement among many financial signals, such as stocks and futures. One investment strategy is to choose less correlated assets. Classic approaches quantifying such relationships in real financial markets make it difficult to exclude factors such as market trends and autocorrelation. In this paper, we propose a signal process perspective for quantitative measurement. A machine learning based algorithm is designed to model returns, taking account of market sensitivity, autocorrelation, and relationships with other stocks. We then extend the model training algorithm using regularized least square and gradient descent to estimate parameters. A penalty factor is designed in the optimization function to address extreme large negative returns. After denoising common factors, the learned pure relationship parameters are applied to construct a relationship matrix. Finally, we use this matrix to build portfolios by constrained optimization. Empirical experiments on two stock datasets show that the proposed method outperforms several state-of-the-art methods in terms of mean average precision and cumulative returns.

Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt obligation promise, if one enterprise in the guarantee network falls into a financial crisis, the debt risk may spread like a virus across the guarantee network, even lead to a systemic financial crisis. In this paper, we propose an imbalanced network risk diffusion model to forecast the enterprise default risk in a short future. Positive weighted k-nearest neighbors (p-wkNN) algorithm is developed for the stand-alone case - when there is no default contagious; then a data-driven default diffusion model is integrated to further improve the prediction accuracy. We perform the empirical study on a real-world three-years loan record from a major commercial bank. The results show that our proposed method outperforms conventional credit risk methods in terms of AUC. In summary, our quantitative risk evaluation model shows promising prediction performance on real-world data, which could be useful to both regulators and stakeholders.

Automatic feature recognition aids downstream processes such as engineering analysis and manufacturing planning. Not all features can be defined in advance; a declarative approach allows engineers to specify new features without having to design algorithms to find them. Naive translation of declarations leads to executable algorithms with high time complexity. Database queries are also expressed declaratively; there is a large literature on optimizing query plans for efficient execution of database queries. Our earlier work investigated applying such technology to feature recognition, using a testbed interfacing a database system (SQLite) to a CAD modeler (CADfix). Feature declarations were translated into SQL queries which are then executed. The current paper extends this approach, using the PostgreSQL database, and provides several new insights: (i) query optimization works quite differently in these two databases, (ii) with care, an approach to query translation can be devised that works well for both databases, and (iii) when finding various simple common features, linear time performance can be achieved with respect to model size, with acceptable times for real industrial models. Further results also show how (i) lazy evaluation can be used to reduce the work performed by the CAD modeler, and (ii) estimating the time taken to compute various geometric operations can further improve the query plan. Experimental results are presented to validate our main conclusions.

Saliency mechanism has been considered crucial in the human visual system and helpful to object detection and recognition. This paper addresses a novel feature-based model for visual saliency detection. It consists of two steps: first, using the learned overcomplete sparse bases to represent image patches; and then, estimating saliency information via direct low-rank and sparsity matrix decomposition. We compare our model with the previous methods on natural images. Experimental results show that our model performs competitively for visual saliency detection task, and suggest the potential application of matrix decomposition and convex optimization for image analysis.

In the presence of non-gaussian noise, we propose a method for the detection of underwater ship-radiated signal. The wavelet decomposition of the underwater signal yields a natural tree structure, which is further modeled by the Hidden Markov Tree (HMT). Therefore, the signal is represented as the parameter of the correspondent HMT. We analysis the likelihood defined on the parameters and form the new detection criteria. Experimental results demonstrate a reliable and robust solution of our method.