Summary Community detection is a fundamental research problem in social networks. However, most existing research focuses on homogeneous networks while ignoring the multitopology and attributes in social media. In this article, we propose community detection algorithms based on community kernels to detect high‐quality communities in heterogeneous social networks. It is noticed that the social community has multiple topology structures, as nodes or users in social media networks have multiple attributions. For example, users can be friends and coworkers in a research group simultaneously. Hence, we propose a multilayer and attribute combined measure (MACM), a novel measurement based on the multilayer structure and common neighboring attributes, which includes the similarity measure between nodes and the importance measure for individual node in multilayer networks. Two improved community kernel detection algorithms based on MACM are subsequently proposed. They are the MA‐Greedy, which is based on the greedy algorithm, and the MA‐WeBA, which is a weighted balanced algorithm. The multilayer structure and attributes are comprehensively considered when calculating the similarity and importance of nodes in these strategies. Extensive experimental results on two public data sets demonstrate that the multilayer structure and attribute information can be used to enhance the precision of community detection.

Word sense disambiguation is a challenging task that aims to distinguish the correct sense for a target word. Unlike typical methods which only use the context information, we present the basic idea that incorporates the global sense distribution and contextual sense dependency to determine the correct sense. In this paper, we leverage the generative process of the sequence topic model to model the sense distribution from the whole global corpus directly. Since contextual sense dependency is more likely to happen between successive words, we hypothesize that the sense assignment of the target word depends on the sense of the previous word. Hence, the sense dependency could be modeled by the probabilistic topic model with hidden Markov chain assumption. Furthermore, the information in the sense inventory of WordNet is used as prior knowledge for obtaining the non-uniform sense distribution over words. It is notable that the prior knowledge contributes to parameter learning and inference during the Gibbs Sampling procedure, hence we call the proposed method a knowledge-based unsupervised approach. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013, and SemEval-2015 English All-Word WSD datasets and the experimental results show that although the performance of the proposed method is restricted by the sparse problem, it still achieves comparable performance compared with the state-of-the-art knowledge-based approaches.

In the real-world social networks, hashtags are widely applied for understanding the content of an individual microblog. However, users do not always take the initiative in attaching hashtags when posting a microblog so that much effort has been invested for automatically hashtag recommendation. As a new trend, users no longer only post texts but prefer to share with multimodal data, such as images. To deal with these situations, we propose an attention-based multimodal neural network model (AMNN) to learn the representations of multimodal microblogs and recommend relevant hashtags. In this article, we convert the hashtag recommendation task into a sequence generation problem. Then, we propose a hybrid neural network approach to extract the features of both texts and images and incorporate them into the sequence-to-sequence model for hashtag recommendation. Experimental results on the data set collected on Instagram and two public data sets demonstrate that the proposed method outperforms state-of-the-art methods. Our model achieves the best performance in three different metrics: precision, recall, and accuracy. The source code of this article can be obtained from “https://github.com/w5688414/AMNN.”

In recent years, the heterogeneous distributed platform integrating with FPGAs to accelerate computation tasks has been widely studied to deal with the deluge of data. However, most of current works suffer from poor universality and low resource utilization that run specific algorithms with the highly customized structure. Moreover, there are still many challenges, such as data curation, task scheduling, and resource management, which further limit the scalability of a CPU-FPGA distributed platform. In this paper, we present HeteroYARN, an FPGA-accelerated heterogeneous architecture based on YARN platform, which provides resource management and programming support for computing-intensive applications using FPGAs. In particular, the HeteroYARN abstracts FPGA accelerators as general resources and provides programming APIs to utilize those accelerators easily. Our HeteroYARN simplifies the request and usage of FPGA resources to enhance the efficiency of the heterogeneous framework while maintaining previous workflow unchanged. Experimental results using two representative algorithms, K-means and Naive Bayes classifier, which are accelerated by FPGAs, demonstrate the usability of the HeteroYARN framework and show performance speedup improvement by 7.5x (K-means) and 2.3x (Naive Bayes) respectively compared to conventional CPU-only applications provided by Mahout.