4,312 research outputs found
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.Comment: 35 pages, 6 figure
A Survey on Graph Representation Learning Methods
Graphs representation learning has been a very active research area in recent
years. The goal of graph representation learning is to generate graph
representation vectors that capture the structure and features of large graphs
accurately. This is especially important because the quality of the graph
representation vectors will affect the performance of these vectors in
downstream tasks such as node classification, link prediction and anomaly
detection. Many techniques are proposed for generating effective graph
representation vectors. Two of the most prevalent categories of graph
representation learning are graph embedding methods without using graph neural
nets (GNN), which we denote as non-GNN based graph embedding methods, and graph
neural nets (GNN) based methods. Non-GNN graph embedding methods are based on
techniques such as random walks, temporal point processes and neural network
learning methods. GNN-based methods, on the other hand, are the application of
deep learning on graph data. In this survey, we provide an overview of these
two categories and cover the current state-of-the-art methods for both static
and dynamic graphs. Finally, we explore some open and ongoing research
directions for future work
Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks
Graph convolutional networks (GCNs) is a class of artificial neural networks
for processing data that can be represented as graphs. Since financial
transactions can naturally be constructed as graphs, GCNs are widely applied in
the financial industry, especially for financial fraud detection. In this
paper, we focus on fraud detection on cryptocurrency truct networks. In the
literature, most works focus on static networks. Whereas in this study, we
consider the evolving nature of cryptocurrency networks, and use local
structural as well as the balance theory to guide the training process. More
specifically, we compute motif matrices to capture the local topological
information, then use them in the GCN aggregation process. The generated
embedding at each snapshot is a weighted average of embeddings within a time
window, where the weights are learnable parameters. Since the trust networks is
signed on each edge, balance theory is used to guide the training process.
Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the
proposed model outperforms those in the literature
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Temporal graphs are widely used to model dynamic systems with time-varying
interactions. In real-world scenarios, the underlying mechanisms of generating
future interactions in dynamic systems are typically governed by a set of
recurring substructures within the graph, known as temporal motifs. Despite the
success and prevalence of current temporal graph neural networks (TGNN), it
remains uncertain which temporal motifs are recognized as the significant
indications that trigger a certain prediction from the model, which is a
critical challenge for advancing the explainability and trustworthiness of
current TGNNs. To address this challenge, we propose a novel approach, called
Temporal Motifs Explainer (TempME), which uncovers the most pivotal temporal
motifs guiding the prediction of TGNNs. Derived from the information bottleneck
principle, TempME extracts the most interaction-related motifs while minimizing
the amount of contained information to preserve the sparsity and succinctness
of the explanation. Events in the explanations generated by TempME are verified
to be more spatiotemporally correlated than those of existing approaches,
providing more understandable insights. Extensive experiments validate the
superiority of TempME, with up to 8.21% increase in terms of explanation
accuracy across six real-world datasets and up to 22.96% increase in boosting
the prediction Average Precision of current TGNNs.Comment: Accepted at NeurIPS 2023, Camera Ready Versio
GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces
We focus on the problem of generating high-quality, private synthetic glucose
traces, a task generalizable to many other time series sources. Existing
methods for time series data synthesis, such as those using Generative
Adversarial Networks (GANs), are not able to capture the innate characteristics
of glucose data and cannot provide any formal privacy guarantees without
severely degrading the utility of the synthetic data. In this paper we present
GlucoSynth, a novel privacy-preserving GAN framework to generate synthetic
glucose traces. The core intuition behind our approach is to conserve
relationships amongst motifs (glucose events) within the traces, in addition to
temporal dynamics. Our framework incorporates differential privacy mechanisms
to provide strong formal privacy guarantees. We provide a comprehensive
evaluation on the real-world utility of the data using 1.2 million glucose
traces; GlucoSynth outperforms all previous methods in its ability to generate
high-quality synthetic glucose traces with strong privacy guarantees
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks
Nowadays, many network representation learning algorithms and downstream
network mining tasks have already paid attention to dynamic networks or
temporal networks, which are more suitable for real-world complex scenarios by
modeling evolving patterns and temporal dependencies between node interactions.
Moreover, representing and mining temporal networks have a wide range of
applications, such as fraud detection, social network analysis, and drug
discovery. To contribute to the network representation learning and network
mining research community, in this paper, we generate a new biological dataset
of dynamic protein-protein interaction networks (i.e., DPPIN), which consists
of twelve dynamic protein-level interaction networks of yeast cells at
different scales. We first introduce the generation process of DPPIN. To
demonstrate the value of our published dataset DPPIN, we then list the
potential applications that would be benefited. Furthermore, we design dynamic
local clustering, dynamic spectral clustering, dynamic subgraph matching,
dynamic node classification, and dynamic graph classification experiments,
where DPPIN indicates future research opportunities for some tasks by
presenting challenges on state-of-the-art baseline algorithms. Finally, we
identify future directions for improving this dataset utility and welcome
inputs from the community. All resources of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN
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