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Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
[article]
2019
arXiv
pre-print
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In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs. ...
First, via a novel approach based on adversarial learning, we compute graph embeddings by training an autoencoder to represent graphs on CCMs. ...
Acknowledgements This research is funded by the Swiss National Science Foundation project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems". ...
arXiv:1805.06299v3
fatcat:v3xpdicj3zg6higeso2gwmij4u
Adversarial Autoencoders with Constant-Curvature Latent Manifolds
[article]
2019
arXiv
pre-print
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Constant-curvature Riemannian manifolds (CCMs) have been shown to be ideal embedding spaces in many application domains, as their non-Euclidean geometry can naturally account for some relevant properties ...
While a few works in recent literature make use of either hyperspherical or hyperbolic manifolds for different learning tasks, ours is the first unified framework to seamlessly deal with CCMs of different ...
This research is funded by the Swiss National Science Foundation project ALPSFORT (200021/172671). ...
arXiv:1812.04314v2
fatcat:gi53rlqla5gnvavb2cwqslawqe
Error Metrics for Learning Reliable Manifolds from Streaming Data
[chapter]
2017
Proceedings of the 2017 SIAM International Conference on Data Mining
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2016 pre-print arXiv:1611.04067v1 fatcat:zeswhzwadrdlrbfytv5rml7qaqOther Versions
In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly less costly manner. ...
We present error metrics that allow us to identify the transition point for a given stream by quantitatively assessing the quality of a manifold learned using Isomap. ...
Acknowledgment This work was supported by NSF grant CNS:1409551. Access to computing facilities were provided by the UB Center for Computational Research. Usual disclaimers apply. ...
doi:10.1137/1.9781611974973.84
dblp:conf/sdm/SchoenemanMCNZ17
fatcat:3yiuyy7yyfdane3htuocje2q5e
Probabilistic modelling of general noisy multi-manifold data sets
2021
Artificial Intelligence
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To the best of our knowledge, this work represents the first attempt at detection and modelling of a set of coexisting general noisy manifolds by uniting two aspects of multi-manifold learning: the recovery ...
We demonstrate the workings of the framework on two synthetic data sets, presenting challenging features for state-of-the-art techniques in Multi-Manifold learning. ...
PT and MC were supported by the Alan Turing Institute Fellowship 96102. ...
doi:10.1016/j.artint.2021.103579
fatcat:hcifg7bgvfcindqria767e76lu
Constant Curvature Graph Convolutional Networks
[article]
2020
arXiv
pre-print
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Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. ...
We do this by i) introducing a unified formalism that can interpolate smoothly between all geometries of constant curvature, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean ...
Gary Bécigneul is funded by the Max Planck ETH Center for Learning Systems. ...
arXiv:1911.05076v3
fatcat:qfvdahbvxbakpjacnq3jgtcfpq
Unsupervised Detection of Changes in Usage-Phases of a Mobile App
2020
Applied Sciences
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This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. ...
Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. ...
In order to represent graphs as points in a non-Euclidean space and detect changes, adversarial training of autoencoders was employed for graph embeddings on constant-curvature manifolds, and change detection ...
doi:10.3390/app10103656
fatcat:us5frw3hwncplj7gfhli7pgxze
Online Graph Dictionary Learning
[article]
2021
arXiv
pre-print
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Our approach naturally extends to labeled graphs, and is completed by a novel upper bound that can be used as a fast approximation of Gromov Wasserstein in the embedding space. ...
Yet, this analysis is not amenable in the context of graph learning, as graphs usually belong to different metric spaces. ...
This work is supported by the ACA-DEMICS grant of the IDEXLYON, project of the Université de Lyon, PIA operated by ANR-16-IDEX-0005. ...
arXiv:2102.06555v2
fatcat:eqg6e7s3lrfe5g5uuihpryjhwq
An Approximate Model for Event Detection from Twitter Data
2020
IEEE Access
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Earlier research works, addressing these challenges, have tried to capture the contextual information by using the dense vector representations of texts leveraging deep neural word embedding generation ...
The abundance and real-time availability of Twitter data have proved beneficial in detecting events in various domains such as emergency situations, crime detection, public health, place recommendations ...
A sphere can be defined as a Riemannian manifold with constant positive curvature. ...
doi:10.1109/access.2020.3007004
fatcat:wcszgxqurrbe7lt6osgisqtl2q
High Performance Algorithms for Quantum Gravity and Cosmology
[article]
2018
arXiv
pre-print
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We then examine the broader applicability of these algorithms to greedy information routing in random geometric graphs embedded in Lorentzian manifolds, which requires us to find new closed-form solutions ...
The high performance algorithms described herein are the most compact, efficient methods known for representing and analyzing systems modeled well by sets or graphs. ...
which the number of scalar fields in the low-energy 4D theory, known as Kähler moduli, changes by one. ...
arXiv:1805.04463v1
fatcat:xbk732bbzjeann2fpefo5ongdi
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
[article]
2021
arXiv
pre-print
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Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream ...
By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. ...
ACKNOWLEDGMENTS This work is partially supported by ARC FT130101530, NSFC No. 61628206 and Google PhD Fellowship. ...
arXiv:2011.12517v2
fatcat:cbfriruejva2rf3r5of25aitz4
Perspectives on geometric analysis
[article]
2006
arXiv
pre-print
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This is a survey paper on several aspects of differential geometry for the last 30 years, especially in those areas related to non-linear analysis. ...
Chern who had passed away in December 2004. ...
bounded from above by a negative constant, are distance decreasing with constants depending only on the bound on the curvature. ...
arXiv:math/0602363v2
fatcat:ad4gz5qjhbahjjz2qt6zacogzq
Perspectives on geometric analysis
[chapter]
2007
Proceedings of the International Conference on Complex Geometry and Related Fields
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bounded from above by a negative constant, are distance decreasing with constants depending only on the bound on the curvature. ...
The man whom I admire is on the bank of the river. I go against the stream in quest of him, But the way is difficult and turns to the right. I go down the stream in quest of him, and Lo! ...
doi:10.1090/amsip/039/17
fatcat:upv2nxqxw5cbdhtnbixope5unu
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
[article]
2021
IEEE Transactions on Neural Networks and Learning Systems
accepted
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In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal ...
In the end, we have a thorough outlook on several promising research directions. ...
While it fails to capture logical patterns and suffers from constant curvature. Chami et al. ...
doi:10.1109/tnnls.2021.3070843
pmid:33900922
arXiv:2002.00388v4
fatcat:4l2yxnf3wbg4zpzdumduvyr4he
Using Hyperbolic Geometry for FG-NET over Distantly Supervised data
[article]
2022
arXiv
pre-print
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In this paper, we propose to use hyperbolic geometry for FG-NET with the hope that it can help overcoming the noise incurred by distant supervision. ...
For a manifold with curvature
constant K, these operations can be summarized in the equa- 3.5 Complete Model We combine the above-mentioned
tion, given below: ...
Learning surface
tity type classification by jointly learning representations and text patterns for a question answering system. In Proceedings
label embeddings. ...
arXiv:2101.11212v2
fatcat:4tbxz4vnane6fbprrfr3w4lkpq
Representation, Analysis, and Recognition of 3D Humans
2018
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Then, a representation is built on lower-level descriptors that model the information embedded in the data. ...
An increasing importance is also assumed by learning solutions, where hand-crafted descriptors are substituted by deep features that are learned directly from the data. ...
[40] were among the first to focus on the task of 3D face detection by analyzing the surface curvature. ...
doi:10.1145/3182179
fatcat:ds55t4md2na2tibtyg4llerf3q
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