Identifying influential nodes on networks is a challenging task that keeps drawing extensive attention from both academia and industry. The local structural information of target nodes and the influences of their nearest neighbors are known to be relevant, but many algorithms only consider one of these two types of information. Even though few algorithms consider both types of information, they often fail to properly adjust their contributions. In this paper, we address the influential nodes identification problem for directed networks where a node can directly affect its in-neighbors, like the social network of Twitter. We present a general approach to quantify and utilize both types of information, specifically, we use the range of nodes that a target node can directly affect to quantify the local structural information of the target node and quantify the influences of the target node's nearest in-neighbors based on an iterative process. A contribution adjustment strategy is applied to make better use of both types of information. Extensive experiments conducted on 12 networks demonstrate that our algorithm outperforms the benchmarks and can effectively identify fast influencers. Furthermore, compared with social capital, a representative algorithm that also considers both types of information, our algorithm demonstrates superior performance.
Quantifying the economic status of nations is significant for economic development. Apart from the traditional economic indicators, many new, granular and real-time economic indicators based on various socioeconomic and economic datasets are reported. In this paper, by employing the world trade data, we characterize the economic status from three dimensions, including the monetary dimension, the network structure dimension and the trade diversification dimension. The results show that network centrality indicators can explain up to 85.4% of the variance of the GDP, which perform best compared with indicators of the other two dimensions. The results show the importance of the network structure in inferencing the economic status of nations, and provide a new perspective for characterizing and understanding the national economic status.
Aspect-based sentiment analysis (ABSA) aims to analyze the emotional color contained in sentences or documents in more detail by classifying and evaluating different aspects and emotions in the text. However, the current research methods cannot effectively analyze the relationship between aspect words and context and extract grammatical information about sentences. Additionally, the extracted syntactic information is insufficient, and the combination of syntactic and semantic information is inefficient, leaving the model unable to correctly determine aspects' emotional orientations. This paper proposes an aspect-level sentiment analysis based on Fusion Graph Double Convolutional Neural Networks (FGD-GCN) to address these issues. Firstly, FGD-GCN proposes a multi-feature extraction module. Using BERT and bidirectional long-short-term memory models, this module extracts the hidden context between words. In addition, the positional attention module is used to capture important features in sentences, reducing noise and bias. Then, a semantic enhancement module is proposed, which fuses attention-focused information and feature information extracted from graphs to emphasize aspect words and context, and uses CNN model to classify on feature vectors. According to experiment results on three benchmark datasets, the model outperforms previous GCN methods for context-based aspect-level sentiment analysis.
Equal pay is an essential component of gender equality, one of the Sustainable Development Goals of the United Nations. Using resume data of over ten million Chinese online job seekers in 2015, we study the current gender pay gap in China. The results show that on average women only earned 71.57\% of what men earned in China. The gender pay gap exists across all age groups and educational levels. Contrary to the commonly held view that developments in education, economy, and a more open culture would reduce the gender pay gap, the fusion analysis of resume data and socio-economic data presents that they have not helped reach the gender pay equality in China. China seems to be stuck in a place where traditional methods cannot make further progress. Our analysis further shows that 81.47\% of the variance in the gender pay gap can be potentially attributed to discrimination. In particular, compared with the unmarried, both the gender pay gap itself and proportion potentially attributed to discrimination of the married are larger, indicating that married women suffer greater inequality and more discrimination than unmarried ones. Taken together, we suggest that more research attention should be paid to the effect of discrimination in understanding gender pay gap based on the family constraint theory. We also suggest the Chinese government to increase investment in family-supportive policies and grants in addition to female education.
Drug-target interaction (DTI) prediction plays a very important role in drug development and drug discovery. Biochemical experiments or \textit{in vitro} methods are very expensive, laborious and time-consuming. Therefore, \textit{in silico} approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra characteristic information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a method based on low-rank matrix projection to solve the DTI prediction problem. On one hand, when there is no extra characteristic information of drugs or targets, the proposed method utilizes only the known interactions. On the other hand, the proposed method can also utilize the extra characteristic information when it is available and the performances will be remarkably improved. Moreover, the proposed method can predict the interactions associated with new drugs or targets of which we know nothing about their associated interactions, but only some characteristic information. We compare the proposed method with ten baseline methods, e.g., six similarity-based methods that utilize only the known interactions and four methods that utilize the extra characteristic information. The datasets and codes implementing the simulations are available at this https URL.
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