1,689 research outputs found
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
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