36,658 research outputs found
Which way? Direction-Aware Attributed Graph Embedding
Graph embedding algorithms are used to efficiently represent (encode) a graph
in a low-dimensional continuous vector space that preserves the most important
properties of the graph. One aspect that is often overlooked is whether the
graph is directed or not. Most studies ignore the directionality, so as to
learn high-quality representations optimized for node classification. On the
other hand, studies that capture directionality are usually effective on link
prediction but do not perform well on other tasks. This preliminary study
presents a novel text-enriched, direction-aware algorithm called DIAGRAM ,
based on a carefully designed multi-objective model to learn embeddings that
preserve the direction of edges, textual features and graph context of nodes.
As a result, our algorithm does not have to trade one property for another and
jointly learns high-quality representations for multiple network analysis
tasks. We empirically show that DIAGRAM significantly outperforms six
state-of-the-art baselines, both direction-aware and oblivious ones,on link
prediction and network reconstruction experiments using two popular datasets.
It also achieves a comparable performance on node classification experiments
against these baselines using the same datasets
Combining Static and Dynamic Features for Multivariate Sequence Classification
Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another type of data. In real-life scenarios, however, it is often the case
that both static and dynamic features are present, or can be extracted from the
data. In this work, we demonstrate how generative models such as Hidden Markov
Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
be used to extract temporal information from the dynamic data. We explore how
the extracted information can be combined with the static features in order to
improve the classification performance. We evaluate the existing techniques and
suggest a hybrid approach, which outperforms other methods on several public
datasets.Comment: Presented at IEEE DSAA 201
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