Link prediction in multilayer networks aims to predict missing links at the target layer by incorporating structural information from both auxiliary layers and the target layer. Existing methods tend to learn layer-specific knowledge to maximize the link prediction performance on a specific network layer. However, they have difficulty incorporating multilayer structural information to improve the link prediction performance. Therefore, we propose a Multiple Attention Layer-shareable Method (MALM) for link prediction in multilayer networks, which consists of a feature encoder, a knowledge learner, and a fusion predictor. The feature encoder introduces multiple attention mechanisms to encode the feature representations of links by differentiating the importance of structural information for each link. In cooperation with the feature encoder, the knowledge learner splits the link prediction tasks into different layers and employs meta-learning to learn layer-shareable knowledge from these link prediction tasks. Finally, the fusion predictor combines the learned layer-shareable knowledge with the layer-specific knowledge at the target layer for link prediction. Experiments on real-world datasets demonstrate that the proposed MALM outperforms existing state-of-the-art baselines in link prediction in multilayer networks.

Link prediction has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network ( TDAN ) based on transfer learning to handle the challenge. TDAN exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types. TDAN mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that TDAN can outperform the state-of-the-art models.

Traditional information retrieval (IR) models, in which a document is normally represented as a bag of words and their frequencies, capture the term-level and document-level information. Topic models, on the other hand, discover semantic topic-based information among words. In this paper, we consider term-based information and semantic information as two features of query terms and propose a simple enhancement for ad-hoc IR via topic modeling. In particular, three topic-based hybrid models, LDA-BM25, LDA-MATF and LDA-LM, are proposed. A series of experiments on eight standard datasets show that our proposed models can always outperform significantly the corresponding strong baselines over all datasets in terms of MAP and most of datasets in terms of [email protected] and [email protected] A direct comparison on eight standard datasets also indicates our proposed models are at least comparable to the state-of-the-art approaches.

In recent years, online social networks are among the most popular websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence generate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity. Compared with the extremely large data over the whole collection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenarios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effective learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth documents based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our approach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP measurements.

Manifold ranking is one of the most competitive approaches for query-focused multi-document summarization. Despite its success for this task, it usually constructs a sentence affinity graph first based on inter-sentence content similarity, and then perform manifold ranking on the graph to score each sentence with the assumption that all the sentences live on a single manifold. Actually, for a document set to be summarized, the distribution of the sentences might form different, but related manifolds. This paper aims to generalize the basic manifold-ranking based approach to the more generic setting by introducing a novel affinity graph to estimate the similarity between sentences, which leverages both the local geometric structures and the contents of sentences jointly. Preliminary experimental results on the DUC datasets demonstrate the good effectiveness of the proposed approach.