196,085 research outputs found
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of
elements, the problem of temporal sets prediction aims to predict the elements
in the subsequent set. In practice, temporal sets prediction is much more
complex than predictive modelling of temporal events and time series, and is
still an open problem. Many possible existing methods, if adapted for the
problem of temporal sets prediction, usually follow a two-step strategy by
first projecting temporal sets into latent representations and then learning a
predictive model with the latent representations. The two-step approach often
leads to information loss and unsatisfactory prediction performance. In this
paper, we propose an integrated solution based on the deep neural networks for
temporal sets prediction. A unique perspective of our approach is to learn
element relationship by constructing set-level co-occurrence graph and then
perform graph convolutions on the dynamic relationship graphs. Moreover, we
design an attention-based module to adaptively learn the temporal dependency of
elements and sets. Finally, we provide a gated updating mechanism to find the
hidden shared patterns in different sequences and fuse both static and dynamic
information to improve the prediction performance. Experiments on real-world
data sets demonstrate that our approach can achieve competitive performances
even with a portion of the training data and can outperform existing methods
with a significant margin.Comment: 9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD '2020
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
Knowledge Graph Completion (KGC) has been recently extended to multiple
knowledge graph (KG) structures, initiating new research directions, e.g.
static KGC, temporal KGC and few-shot KGC. Previous works often design KGC
models closely coupled with specific graph structures, which inevitably results
in two drawbacks: 1) structure-specific KGC models are mutually incompatible;
2) existing KGC methods are not adaptable to emerging KGs. In this paper, we
propose KG-S2S, a Seq2Seq generative framework that could tackle different
verbalizable graph structures by unifying the representation of KG facts into
"flat" text, regardless of their original form. To remedy the KG structure
information loss from the "flat" text, we further improve the input
representations of entities and relations, and the inference algorithm in
KG-S2S. Experiments on five benchmarks show that KG-S2S outperforms many
competitive baselines, setting new state-of-the-art performance. Finally, we
analyze KG-S2S's ability on the different relations and the Non-entity
Generations.Comment: COLING 2022 Main Conferenc
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Node Embedding over Temporal Graphs
In this work, we present a method for node embedding in temporal graphs. We
propose an algorithm that learns the evolution of a temporal graph's nodes and
edges over time and incorporates this dynamics in a temporal node embedding
framework for different graph prediction tasks. We present a joint loss
function that creates a temporal embedding of a node by learning to combine its
historical temporal embeddings, such that it optimizes per given task (e.g.,
link prediction). The algorithm is initialized using static node embeddings,
which are then aligned over the representations of a node at different time
points, and eventually adapted for the given task in a joint optimization. We
evaluate the effectiveness of our approach over a variety of temporal graphs
for the two fundamental tasks of temporal link prediction and multi-label node
classification, comparing to competitive baselines and algorithmic
alternatives. Our algorithm shows performance improvements across many of the
datasets and baselines and is found particularly effective for graphs that are
less cohesive, with a lower clustering coefficient
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding
Human action recognition refers to automatic recognizing human actions from a
video clip. In reality, there often exist multiple human actions in a video
stream. Such a video stream is often weakly-annotated with a set of relevant
human action labels at a global level rather than assigning each label to a
specific video episode corresponding to a single action, which leads to a
multi-label learning problem. Furthermore, there are many meaningful human
actions in reality but it would be extremely difficult to collect/annotate
video clips regarding all of various human actions, which leads to a zero-shot
learning scenario. To the best of our knowledge, there is no work that has
addressed all the above issues together in human action recognition. In this
paper, we formulate a real-world human action recognition task as a multi-label
zero-shot learning problem and propose a framework to tackle this problem in a
holistic way. Our framework holistically tackles the issue of unknown temporal
boundaries between different actions for multi-label learning and exploits the
side information regarding the semantic relationship between different human
actions for knowledge transfer. Consequently, our framework leads to a joint
latent ranking embedding for multi-label zero-shot human action recognition. A
novel neural architecture of two component models and an alternate learning
algorithm are proposed to carry out the joint latent ranking embedding
learning. Thus, multi-label zero-shot recognition is done by measuring
relatedness scores of action labels to a test video clip in the joint latent
visual and semantic embedding spaces. We evaluate our framework with different
settings, including a novel data split scheme designed especially for
evaluating multi-label zero-shot learning, on two datasets: Breakfast and
Charades. The experimental results demonstrate the effectiveness of our
framework.Comment: 27 pages, 10 figures and 7 tables. Technical report submitted to a
journal. More experimental results/references were added and typos were
correcte
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