Leveraging Social Foci for Information Seeking in Social Media

The rise of social media provides a great opportunity for people to reach out to their social connections to satisfy their information needs. However, generic social media platforms are not explicitly designed to assist information seeking of users. In this paper, we propose a novel framework to identify the social connections of a user able to satisfy his information needs. The information need of a social media user is subjective and personal, and we investigate the utility of his social context to identify people able to satisfy it. We present questions users post on Twitter as instances of information seeking activities in social media. We infer soft community memberships of the asker and his social connections by integrating network and content information. Drawing concepts from the social foci theory, we identify answerers who share communities with the asker w.r.t. the question. Our experiments demonstrate that the framework is effective in identifying answerers to social media questions.Comment: AAAI 201

Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page

2-Phenyl­imidazolium chloride monohydrate

In the title hydrated molecular salt, C9H9N2 +·Cl−·H2O, the dihedral angle between the five- and six-membered rings in the cation is 18.00 (2)°. O—H⋯Cl, N—H⋯O and N—H⋯Cl hrdrogen-bonding inter­actions are present in the crystal structure

2-Phenyl­imidazolium acetate

There are two 2-phenyl­imidazole cations and two acetate anions in the asymmetric unit of the title mol­ecular salt, C9H9N2 +·C2H3O2 −. The dihredral angles between the five- and six-membered rings are 5.50 (2) and 6.90 (2)° in the two molecules. The structure is stabilized by N—H⋯O and weak C—H⋯O hydrogen-bonding inter­actions between the cations and anions, resulting in chains propagating in [110]

2-Phenyl­imidazole dihydrogen phosphate phospho­ric acid

The crystal structure of the title compound, C9H9N2 +·H2PO4 −·H3PO4, is stabilized by N—H⋯O and O—H⋯O hydrogen-bonding inter­actions, resulting in a two-dimensional network