Multi-FPGA architectures have gained great interests in accelerating large-scale graph processing with great potential on high throughput and energy efficiency. As a beneficial complement, work stealing functions effectively to balance the computationa…
Motivation: The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learni…
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph-structured data. However, as previous methods usually focus on learning embedding for a single network, they cannot learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this article, we propose a Domain Adaptive Network Embedding framework, which applies Graph Convolutional Network to learn transferable embedding. In DANE, nodes from multiple networks are encoded to vectors via a shared and aligned embedding space. The distribution of embedding on different networks are further aligned by Adversarial Learning Regularization. To achieve better performance in scenarios where labels are provided, DANE adopts a cross-entropy error term of the GCN framework and class centroid aligning method. Moreover, DANE’s advantages in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other well-recognized network embedding baselines in cross-network domain adaptation tasks, and the semi-supervised components improve the performance significantly.
Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation …
Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, is an important aspect of geographic information flow analysis, because the density of such information can reflect the urbanization variables such as population, gr…
Medical concept normalization is a critical problem in biomedical research and clinical applications. In this article, we focus on normalizing diagnostic and operation names in Chinese discharge summaries to standard concepts, which is formulated as a …
Many iterative graph processing systems have recently been developed to analyze graphs. Although they are effective from different aspects, there is an important issue that has not been addressed yet. A real-world graph follows the power-law property, …
The speed and volume at which misinformation spreads on social media have motivated efforts to automate fact-checking which begins with stance detection. For fake news stance detection, for example, many classification-based models have been proposed o…
When deploying applications with dynamic and intensive memory footprint to big data systems on public clouds, one important yet challenging question to answer is how to select a specific instance type whose memory capacity is large enough to prevent ou…
Vehicular ad-hoc networks (VANETs) have been widely studied in intelligent transportation. Content delivery is an important topic that attracts many researchers. Due to vehicles that may have intermittent connections and uncertain routes, it is difficu…