1,179 research outputs found
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
The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks
We consider the problem of analyzing timestamped relational events between a
set of entities, such as messages between users of an on-line social network.
Such data are often analyzed using static or discrete-time network models,
which discard a significant amount of information by aggregating events over
time to form network snapshots. In this paper, we introduce a block point
process model (BPPM) for continuous-time event-based dynamic networks. The BPPM
is inspired by the well-known stochastic block model (SBM) for static networks.
We show that networks generated by the BPPM follow an SBM in the limit of a
growing number of nodes. We use this property to develop principled and
efficient local search and variational inference procedures initialized by
regularized spectral clustering. We fit BPPMs with exponential Hawkes processes
to analyze several real network data sets, including a Facebook wall post
network with over 3,500 nodes and 130,000 events.Comment: To appear at The Web Conference 201
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Temporal graph learning aims to generate high-quality representations for
graph-based tasks along with dynamic information, which has recently drawn
increasing attention. Unlike the static graph, a temporal graph is usually
organized in the form of node interaction sequences over continuous time
instead of an adjacency matrix. Most temporal graph learning methods model
current interactions by combining historical information over time. However,
such methods merely consider the first-order temporal information while
ignoring the important high-order structural information, leading to
sub-optimal performance. To solve this issue, by extracting both temporal and
structural information to learn more informative node representations, we
propose a self-supervised method termed S2T for temporal graph learning. Note
that the first-order temporal information and the high-order structural
information are combined in different ways by the initial node representations
to calculate two conditional intensities, respectively. Then the alignment loss
is introduced to optimize the node representations to be more informative by
narrowing the gap between the two intensities. Concretely, besides modeling
temporal information using historical neighbor sequences, we further consider
the structural information from both local and global levels. At the local
level, we generate structural intensity by aggregating features from the
high-order neighbor sequences. At the global level, a global representation is
generated based on all nodes to adjust the structural intensity according to
the active statuses on different nodes. Extensive experiments demonstrate that
the proposed method S2T achieves at most 10.13% performance improvement
compared with the state-of-the-art competitors on several datasets
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
Audiovisual data is everywhere in this digital age, which raises higher
requirements for the deep learning models developed on them. To well handle the
information of the multi-modal data is the key to a better audiovisual modal.
We observe that these audiovisual data naturally have temporal attributes, such
as the time information for each frame in the video. More concretely, such data
is inherently multi-modal according to both audio and visual cues, which
proceed in a strict chronological order. It indicates that temporal information
is important in multi-modal acoustic event modeling for both intra- and
inter-modal. However, existing methods deal with each modal feature
independently and simply fuse them together, which neglects the mining of
temporal relation and thus leads to sub-optimal performance. With this
motivation, we propose a Temporal Multi-modal graph learning method for
Acoustic event Classification, called TMac, by modeling such temporal
information via graph learning techniques. In particular, we construct a
temporal graph for each acoustic event, dividing its audio data and video data
into multiple segments. Each segment can be considered as a node, and the
temporal relationships between nodes can be considered as timestamps on their
edges. In this case, we can smoothly capture the dynamic information in
intra-modal and inter-modal. Several experiments are conducted to demonstrate
TMac outperforms other SOTA models in performance. Our code is available at
https://github.com/MGitHubL/TMac.Comment: This work has been accepted by ACM MM 2023 for publicatio
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