Hi everyone,
Please join us for our next DM-Meeting.
****DM-Meeting Spring 2017****
WHO: Xinfeng Xu
WHEN: Monday, Apr. 3, 2017 @ 4:00 pm
WHERE: Torg 3160 A
WHAT: Xinfeng will talk about the following two papers:
*1. Accelerated Attributed Network Embedding (SDM 2017)*
*Abstract*: Network embedding is to learn low-dimensional vector
representations for nodes in a network. It has shown to be effective in a
variety of tasks such as node classification and link prediction. While
embedding algorithms on pure networks have been intensively studied, in
many real-world applications, nodes are often accompanied with a rich set
of attributes or features, aka attributed networks. It has been observed
that network topological structure and node attributes are often strongly
correlated with each other. Thus modeling and incorporating node attribute
proximity into network embedding could be potentially helpful, though
non-trivial, in learning better vector representations. Meanwhile,
real-world networks often contain a large number of nodes and features,
which put demands on the scalability of embedding algorithms. To bridge the
gap, in this paper, we propose an accelerated attributed network embedding
algorithm AANE, which enables the joint learning process to be done in a
distributed manner by decomposing the complex modeling and optimization
into many sub-problems. Experimental results on several real-world datasets
demonstrate the effectiveness and efficiency of the proposed algorithm.
*2. Embedding of Embedding (EOE) : Joint Embedding for Coupled
Heterogeneous Networks(WSDM 2017)*
*Abstract*: Network embedding is increasingly employed to assist network
analysis as it is effective to learn latent features that encode linkage
information. Various network embedding methods have been proposed, but they
are only designed for a single network scenario. In the era of big data,
different types of related information can be fused together to form a
coupled heterogeneous network, which consists of two different but related
sub-networks connected by inter-network edges. In this scenario, the
inter-network edges can act as complementary information in the presence of
intra-network ones. This complementary information is important because it
can make latent features more comprehensive and accurate. And it is more
important when the intra-network edges are absent, which can be referred to
as the cold-start problem. In this paper, we thus propose a method named
embedding of embedding (EOE) for coupled heterogeneous networks. In the
EOE, latent features encode not only intra-network edges, but also
inter-network ones. To tackle the challenge of heterogeneities of two
networks, the EOE incorporates a harmonious embedding matrix to further
embed the embeddings that only encode intra-network edges. Empirical
experiments on a variety of real-world datasets demonstrate the EOE
outperforms consistently single network embedding methods in applications
including visualization, link prediction multi-class classification, and
multi-label classification.
Best regards,
Sorour
