Xiangfeng Meng

Multi-view clustering with exemplars for scientific mapping

AbstractScientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected.

NetRating: Credit Risk Evaluation for Loan Guarantee Chain in China

Guaranteed loans are a common way for enterprises to raise money from banks without any collateral in China. The enterprises are highly intertwined with each other, and hence form a densely connected guarantee network. As the economy is down in recent years, the default risk spreads along with the guarantee relations, and has caused great financial risk in many regions of China. Thus it puts forward a new challenge for financial regulators to monitor the enterprises involved in the guarantee network and control the system risk. However, the traditional financial risk management are based on vector space models, and could not handle the relations among enterprises. In this paper, based on the k-shell decomposition method, we propose a novel risk evaluation strategy, NetRating, to assess the risk level of each enterprise involved in the guaranteed loans. Besides, to deal with the direct guarantee networks, we propose the directed k-shell decomposition method, and extend NetRating strategy to the directed NetRating strategy. The application of our strategy in the real data verifies its effectiveness in credit assessment. It indicates that our strategy can provide a novel perspective for financial regulators to monitor the guarantee networks and control potential system risk.

A Structural Based Community Similarity Algorithm and Its Application in Scientific Event Detection

Graph similarity has been a crucial topic in network science, and is widely used in network dynamics, graph monitoring and anomalous event detection. However, few studies have paid attention to community similarity. The fact that communities do not necessarily own sub-modularity structure determines that graph similarity algorithms can not be applied to communities directly. Besides, the existing graph similarity algorithms ignore the organization structure of networks. Two communities can be regarded as the same when both their vertices and structure are identical. Thus the existing algorithms are unable to detect anomalous events about the shift of communities’ organization structure. In this paper, we propose a novel community similarity algorithm, which considers both the shift of vertices and the shift of communities’ layered structure. The layered structure of communities categorizes nodes into different groups, depending on their influence in the community. Both the influence of each node and the shift of nodes’ influence are expected to affect the similarity of two communities. Experiments on the synthetic data show that the novel algorithm performs better than the state-of-art algorithms. Besides, we apply the novel algorithm on the scientific data set, and identify meaningful anomalous events occurred in scientific mapping. The anomalous events are proved to correspond to the transition of topics for journal communities. It demonstrates that the novel algorithm is effective in detecting the anomalous events about the transition of communities’ structure.

Online adaptive method for disease prediction based on big data of clinical laboratory test

To better utilize the medical data in electronic medical records (EMR), this study aims to present an online adaptive method for disease prediction based on the medical data of clinical laboratory test (CLT) items stored in EMR. We firstly extract the diagnosis and CLT items information from the system, and then divide the CLT items into three categories to establish the patterns of CLT items, which are subsequently used for the selection of candidate diseases. A binary relevance approach based on logistic sparse group lasso method is finally used for disease prediction. Four groups of 21,288 patients with diagnosis of chronic hepatitis, hyperuricemia, hyperlipidemia and random diseases are used to test the performance of our method. Results show that the accuracy and recall for these four groups are all above 70%. As a primary attempt to practice intelligent healthcare, this model may have the potential values of computer-aided diagnosis. Further studies are suggested to combine CLT with other types of EMR data to further improve the prediction performance.

Adaptive logistic group Lasso method for predicting the no-reflow among the multiple types of high-dimensional variables with missing data

The prediction of no-reflow phenomenon aroused much attention, because of its independent association with increased in-hospital mortality, malignant arrhythmias, and cardiac failure. Many studies on prediction of no-reflow were carried out focusing on only few predictors. As big data era has been coming, high-dimensional predictors are available for prediction. However, as a common problem, big data analytics in healthcare from the electronic medical record (EMR) system is faced with many challenges, such as missing data processing, multiple types of variables processing and the high-dimensional data prediction. A general method based on improved weighted K-nearest neighbors and adaptive logistic group Lasso was proposed for predicting the no-reflow after cardiac surgery among the multiple types of variables with missing data. Compared with logistic regression, Lasso method, and artificial neural network method, our method has lower misclassification error rate and less complex model for no-reflow prediction, especially when predicting among multiple types of variables with missing data.

A novel dynamic community detection algorithm based on modularity optimization

Dynamic community detection has been an attractive topic due to its ability to reveal the evolutionary trends over time. However, existing dynamic community detection algorithms suffer from several disadvantages. Some make strong assumptions about the generation of communities, or require priori knowledge. In this paper, we propose a novel algorithm, dynamic Louvain method, to detect communities in temporal networks based on modularity optimization. The basic motivation is that the communities across different time steps should smoothly evolve. When partitioning temporal networks at a given time step, we should take historical network structure into consideration. Besides, this algorithm makes no assumption about the generation of communities, and is able to decide the number of communities automatically. This novel algorithm is applied to the temporal financial networks, and numerical evaluations show that this novel algorithm could obtain better partitions, compared with other state-of-art algorithms.