SimCC: A novel method to consider both content and citations for computing similarity of scientific papers

Link prediction based on spectral analysis

Link prediction in complex network is an important issue in network science. Recently, various structure-based similarity methods have been proposed. Most of algorithms are used to analyze the topology of the network, and to judge whether there is any connection between nodes by calculating the similarity of two nodes. However, it is necessary to get the extra attribute information of the node in advance, which is very difficult. Compared to the difficulty in obtaining the attribute information of the node itself, the topology of the network is easy to obtain, and the structure of the network is an inherent attribute of the network and is more reliable. The proposed method measures kinds of similarity between nodes based on non-trivial eigenvectors of Laplacian Matrix of the network, such as Euclidean distance, Manhattan distance and Angular distance. Then the classical machine learning algorithm can be used for classification prediction (two classification in this case), so as to achieve the purpose of link prediction. Based on this process, a spectral analysis-based link prediction algorithm is proposed, and named it LPbSA (Link Prediction based on Spectral Analysis). The experimental results on seven real-world networks demonstrated that LPbSA has better performance on Accuracy, Precision, Receiver Operating Curve(ROC), area under the ROC curve(AUC), Precision and Recall curve(PR curve) and balanced F Score(F-score curve) evaluation metrics than other ten classic methods.

The effect of co-opinion on the cocitation-based information retrieval systems’ effectiveness evaluated by semantic similarity

The co-opinionatedness measure, that is, the similarity of cociting documents in their opinions about their cocited articles, has been recently proposed. The present study uses a wider range of baselines and benchmarks to investigate the measure’s effectiveness in retrieval ranking that was previously confirmed in a pilot study. A test collection was built including 30 seed documents and their 4702 cocited articles. Their citances and full-texts were analysed using natural language processing (NLP) and opinion mining techniques. Cocitation values, syntactical similarity and contexts similarity were used as baselines. The distributional semantic similarity and the linear and hierarchical Medical Subject Headings (MeSH) similarities served as benchmarks to evaluate the effect of the co-opinionatedness as a boosting factor on the performance of the baselines. The improvements in the rankings were measured by normalised discounted cumulative gain (nDCG). According to the findings, there existed significant differences between the nDCG mean values obtained before and after weighting the baselines by the co-opinionatedness measures. The results of the generalisability study corroborated the reliability and generalisability of the systems. Accordingly, the similarity in the opinions of the cociting papers towards their cocited articles can explain the cocitation relation in the scientific papers network and can be effectively utilised for improving the results of the cocitation-based retrieval systems.

Enhancing direct citations: A comparison of relatedness measures for community detection in a large set of PubMed publications

The effects of enhancing direct citations, with respect to publication–publication relatedness measurement, by indirect citation relations (bibliographic coupling, cocitation, and extended direct citations) and text relations on clustering solution accuracy are analyzed. For comparison, we include each approach that is involved in the enhancement of direct citations. In total, we investigate the relative performance of seven approaches. To evaluate the approaches we use a methodology proposed by earlier research. However, the evaluation criterion used is based on MeSH, one of the most sophisticated publication-level classification schemes available. We also introduce an approach, based on interpolated accuracy values, by which overall relative clustering solution accuracy can be studied. The results show that the cocitation approach has the worst performance, and that the direct citations approach is outperformed by the other five investigated approaches. The extended direct citations approach has the best performance, followed by an approach in which direct citations are enhanced by the BM25 textual relatedness measure. An approach that combines direct citations with bibliographic coupling and cocitation performs slightly better than the bibliographic coupling approach, which in turn has a better performance than the BM25 approach.