Hi everyone,
Please join us for our next DM-Meeting.
****DM-Meeting Spring 2018****
WHO: Xinfeng Xu
WHEN: Wednesday, April 4th, @ 4:30 pm
WHERE: McB 133C
WHAT: Xinfeng will talk about the following two papers,
Title: Neural Graph Learning: Training Neural Networks Using Graphs (WSDM
18)
ABSTRACT:
Label propagation is a powerful and flexible semi-supervised learning
technique on graphs. Neural networks, on the other hand, have proven track
records in many supervised learning tasks. In this work, we propose a
training framework with a graph-regularised objective, namely Neural Graph
Machines, that can combine the power of neural networks and label
propagation. This work generalizes previous literature on graph-augmented
training of neural networks, enabling it to be applied to multiple neural
architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of
graphs. The new objective allows the neural networks to harness both
labeled and unlabeled data by: (a) allowing the network to train using
labeled data as in the supervised setting, (b) biasing the network to learn
similar hidden representations for neighboring nodes on a graph, in the
same vein as label propagation. Such architectures with the proposed
objective can be trained efficiently using stochastic gradient descent and
scaled to large graphs, with a runtime that is linear in the number of
edges. The proposed joint training approach convincingly outperforms many
existing methods on a wide range of tasks (multi-label classification on
social graphs, news categorization, document classification and semantic
intent classification), with multiple forms of graph inputs (including
graphs with and without node-level features) and using different
types of neural networks.
Title: Multi-Dimensional Network Embedding with Hierarchical Structure
ABSTRACT:
Information networks are ubiquitous in many applications. A popular way to
facilitate the information in a network is to embed the network structure
into low dimension spaces where each node is represented as a vector. The
learned representations have been proven to advance various network
analysis tasks such as link prediction and node classification. The
majority of existing embedding algorithms are designed for the networks
with one type of nodes and one dimension of relations among nodes. However,
many networks in the real-world complex systems have multiple types of
nodes and multiple dimensions of relations. For example, an e-commerce
network can have users and items, and items can be viewed or purchased by
users, corresponding to two dimensions of relations. In addition, some
types of nodes can present hierarchical structure. For example, authors in
publication networks are associated to affiliations; and items in
e-commerce networks belong to categories. Most of existing methods cannot
be naturally applicable to these networks. In this paper, we aim to learn
representations for networks with multiple dimensions and hierarchical
structure. In particular, we provide an approach to capture independent
information from each dimension and dependent information across dimensions
and propose a framework MINES, which performs Multi-dImension Network
Embedding with hierarchical Structure. Experimental results on a network
from a real-world e-commerce website demonstrate the effectiveness of the
proposed framework.
Best regards,
Sorour
