8,595 research outputs found
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
Language in Our Time: An Empirical Analysis of Hashtags
Hashtags in online social networks have gained tremendous popularity during
the past five years. The resulting large quantity of data has provided a new
lens into modern society. Previously, researchers mainly rely on data collected
from Twitter to study either a certain type of hashtags or a certain property
of hashtags. In this paper, we perform the first large-scale empirical analysis
of hashtags shared on Instagram, the major platform for hashtag-sharing. We
study hashtags from three different dimensions including the temporal-spatial
dimension, the semantic dimension, and the social dimension. Extensive
experiments performed on three large-scale datasets with more than 7 million
hashtags in total provide a series of interesting observations. First, we show
that the temporal patterns of hashtags can be categorized into four different
clusters, and people tend to share fewer hashtags at certain places and more
hashtags at others. Second, we observe that a non-negligible proportion of
hashtags exhibit large semantic displacement. We demonstrate hashtags that are
more uniformly shared among users, as quantified by the proposed hashtag
entropy, are less prone to semantic displacement. In the end, we propose a
bipartite graph embedding model to summarize users' hashtag profiles, and rely
on these profiles to perform friendship prediction. Evaluation results show
that our approach achieves an effective prediction with AUC (area under the ROC
curve) above 0.8 which demonstrates the strong social signals possessed in
hashtags.Comment: WWW 201
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