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Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds [article]

Daniele Grattarola, Daniele Zambon, Cesare Alippi, Lorenzo Livi
2019 arXiv   pre-print
In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs.  ...  Among these, constant-curvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric  ...  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]

Daniele Grattarola, Lorenzo Livi, Cesare Alippi
2019 arXiv   pre-print
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  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
arXiv:1812.04314v2 fatcat:gi53rlqla5gnvavb2cwqslawqe

Error Metrics for Learning Reliable Manifolds from Streaming Data [chapter]

Frank Schoeneman, Suchismit Mahapatra, Varun Chandola, Nils Napp, Jaroslaw Zola
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
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.  ...  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

M. Canducci, P. Tiño, M. Mastropietro
2021 Artificial Intelligence  
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.  ...  We propose a framework for dimensionality estimation and reconstruction of multiple noisy manifolds embedded in a noisy environment.  ...  We then evaluated curvatures on all edges of the embedded graphs. The results are presented in Fig. 9 .  ... 
doi:10.1016/j.artint.2021.103579 fatcat:hcifg7bgvfcindqria767e76lu

Constant Curvature Graph Convolutional Networks [article]

Gregor Bachmann, Gary Bécigneul, Octavian-Eugen Ganea
2020 arXiv   pre-print
Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces.  ...  Empirically, we outperform Euclidean GCNs in the tasks of node classification and distortion minimization for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature.  ...  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

Hoyeol Chae, Ryangkyung Kang, Ho-Sik Seok
2020 Applied Sciences  
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]

Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty
2021 arXiv   pre-print
We provide numerical evidences showing the interest of our approach for unsupervised embedding of graph datasets and for online graph subspace estimation and tracking.  ...  Yet, this analysis is not amenable in the context of graph learning, as graphs usually belong to different metric spaces.  ...  The authors are grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support.  ... 
arXiv:2102.06555v2 fatcat:eqg6e7s3lrfe5g5uuihpryjhwq

An Approximate Model for Event Detection from Twitter Data

Aarzoo Dhiman, Durga Toshniwal
2020 IEEE Access  
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  ...  Second, the high computation cost required in event detection due to massive data processing.  ...  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]

William J. Cunningham
2018 arXiv   pre-print
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.  ...  ), constant curvature everywhere, and most importantly, a non-zero value for S EH .  ... 
arXiv:1805.04463v1 fatcat:xbk732bbzjeann2fpefo5ongdi

Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [article]

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Mahsa Baktashmotlagh
2021 arXiv   pre-print
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  ...  , and (3) which social theory to follow in the learning process.  ...  Thanks to Kevin Swersky for valuable discussions on this topic and to the reviewers for their helpful suggestions.  ... 
arXiv:2011.12517v2 fatcat:cbfriruejva2rf3r5of25aitz4

Perspectives on geometric analysis [article]

Shing-Tung Yau
2006 arXiv   pre-print
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.  ...  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!  ... 
arXiv:math/0602363v2 fatcat:ad4gz5qjhbahjjz2qt6zacogzq

Perspectives on geometric analysis [chapter]

2007 Proceedings of the International Conference on Complex Geometry and Related Fields  
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!  ...  bounded from above by a negative constant, are distance decreasing with constants depending only on the bound on the curvature.  ... 
doi:10.1090/amsip/039/17 fatcat:upv2nxqxw5cbdhtnbixope5unu

A Survey on Knowledge Graphs: Representation, Acquisition and Applications [article]

Shaoxiong Ji and Shirui Pan and Erik Cambria and Pekka Marttinen and Philip S. Yu
2021 IEEE Transactions on Neural Networks and Learning Systems   accepted
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.  ...  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  ...  RLvLR-Stream [95] considers temporal close-path rules and learns the structure of rules from the knowledge graph stream for reasoning. VI.  ... 
doi:10.1109/tnnls.2021.3070843 pmid:33900922 arXiv:2002.00388v4 fatcat:4l2yxnf3wbg4zpzdumduvyr4he

Using Hyperbolic Geometry for FG-NET over Distantly Supervised data [article]

Muhammad Asif Ali, Yifang Sun, Bing Li, Wei Wang
2022 arXiv   pre-print
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

Stefano Berretti, Mohamed Daoudi, Pavan Turaga, Anup Basu
2018 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
Then, a representation is built on lower-level descriptors that model the information embedded in the data.  ...  Finally, such representations are the input for a classification stage that can rely on some classifier or be integrated into a (deep) learning framework.  ...  [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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