Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling.

The subtask of CoNLL 2016 Shared Task focuses on sense classification of multilingual shallow discourse relations. Existing systems rely heavily on external resources, hand-engineered features, patterns, and complex pipelines fine-tuned for the English language. In this paper we describe a different approach and system inspired by end-to-end training of deep neural networks. Its input consists of only sequences of tokens, which are processed by our novel focused RNNs layer, and followed by a dense neural network for classification. Neural networks implicitly learn latent features useful for discourse relation sense classification, make the approach almost language-agnostic and independent of prior linguistic knowledge. In the closed-track sense classification task our system achieved overall 0.5246 F 1-measure on English blind dataset and achieved the new state-of-the-art of 0.7292 F 1-measure on Chinese blind dataset.

In the proposed doctoral work we will design an end-to-end approach for the challenging NLP task of text-level discourse parsing. Instead of depending on mostly hand-engineered sparse features and independent components for each subtask, we propose a unified approach completely based on deep learning architectures. To train more expressive representations that capture communicative functions and semantic roles of discourse units and relations between them, we will jointly learn all discourse parsing subtasks at different layers of our architecture and share their intermediate representations. By combining unsupervised training of word embeddings with our layer-wise multi-task learning of higher representations we hope to reach or even surpass performance of current state-of-the-art methods on annotated English corpora.

CRYSTAlLOGR4PBIC STATISTICS Al"D TIlE DEVFLOP-/llFNT OF DIFECT /I1FI'HODS. By J. Vade, laboratory for the Structure of' "'atter,

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