4,281 research outputs found
Network Representation Learning: A Survey
With the widespread use of information technologies, information networks are
becoming increasingly popular to capture complex relationships across various
disciplines, such as social networks, citation networks, telecommunication
networks, and biological networks. Analyzing these networks sheds light on
different aspects of social life such as the structure of societies,
information diffusion, and communication patterns. In reality, however, the
large scale of information networks often makes network analytic tasks
computationally expensive or intractable. Network representation learning has
been recently proposed as a new learning paradigm to embed network vertices
into a low-dimensional vector space, by preserving network topology structure,
vertex content, and other side information. This facilitates the original
network to be easily handled in the new vector space for further analysis. In
this survey, we perform a comprehensive review of the current literature on
network representation learning in the data mining and machine learning field.
We propose new taxonomies to categorize and summarize the state-of-the-art
network representation learning techniques according to the underlying learning
mechanisms, the network information intended to preserve, as well as the
algorithmic designs and methodologies. We summarize evaluation protocols used
for validating network representation learning including published benchmark
datasets, evaluation methods, and open source algorithms. We also perform
empirical studies to compare the performance of representative algorithms on
common datasets, and analyze their computational complexity. Finally, we
suggest promising research directions to facilitate future study.Comment: Accepted by IEEE transactions on Big Data; 25 pages, 10 tables, 6
figures and 127 reference
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
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