Unsupervised Learning via Network-Aware Embeddings
- URL: http://arxiv.org/abs/2309.10408v1
- Date: Tue, 19 Sep 2023 08:17:48 GMT
- Title: Unsupervised Learning via Network-Aware Embeddings
- Authors: Anne Sophie Riis Damstrup, Sofie Tosti Madsen, Michele Coscia
- Abstract summary: We show how to create network-aware embeddings by estimating the network distance between numeric node attributes.
Our method is fully open source and data and code are available to reproduce all results in the paper.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data clustering, the task of grouping observations according to their
similarity, is a key component of unsupervised learning -- with real world
applications in diverse fields such as biology, medicine, and social science.
Often in these fields the data comes with complex interdependencies between the
dimensions of analysis, for instance the various characteristics and opinions
people can have live on a complex social network. Current clustering methods
are ill-suited to tackle this complexity: deep learning can approximate these
dependencies, but not take their explicit map as the input of the analysis. In
this paper, we aim at fixing this blind spot in the unsupervised learning
literature. We can create network-aware embeddings by estimating the network
distance between numeric node attributes via the generalized Euclidean
distance. Differently from all methods in the literature that we know of, we do
not cluster the nodes of the network, but rather its node attributes. In our
experiments we show that having these network embeddings is always beneficial
for the learning task; that our method scales to large networks; and that we
can actually provide actionable insights in applications in a variety of fields
such as marketing, economics, and political science. Our method is fully open
source and data and code are available to reproduce all results in the paper.
Related papers
- Impact of network topology on the performance of Decentralized Federated
Learning [4.618221836001186]
Decentralized machine learning is gaining momentum, addressing infrastructure challenges and privacy concerns.
This study investigates the interplay between network structure and learning performance using three network topologies and six data distribution methods.
We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation.
arXiv Detail & Related papers (2024-02-28T11:13:53Z) - The effect of network topologies on fully decentralized learning: a
preliminary investigation [2.9592782993171918]
In a decentralized machine learning system, data is partitioned among multiple devices or nodes, each of which trains a local model using its own data.
We investigate how different types of topologies impact the "spreading of knowledge"
Specifically, we highlight the different roles in this process of more or less connected nodes (hubs and leaves)
arXiv Detail & Related papers (2023-07-29T09:39:17Z) - FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for
Federated Learning on Non-IID Data [69.0785021613868]
Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos.
We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies.
This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT)
arXiv Detail & Related papers (2022-05-19T03:32:03Z) - Graph Neural Networks: Methods, Applications, and Opportunities [1.2183405753834562]
This article provides a comprehensive survey of graph neural networks (GNNs) in each learning setting.
The approaches for each learning task are analyzed from both theoretical as well as empirical standpoints.
Various applications and benchmark datasets are also provided, along with open challenges still plaguing the general applicability of GNNs.
arXiv Detail & Related papers (2021-08-24T13:46:19Z) - A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
A community reveals the features and connections of its members that are different from those in other communities in a network.
This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods.
The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
arXiv Detail & Related papers (2021-05-26T14:37:07Z) - A neural anisotropic view of underspecification in deep learning [60.119023683371736]
We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation.
Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
arXiv Detail & Related papers (2021-04-29T14:31:09Z) - Anomaly Detection on Attributed Networks via Contrastive Self-Supervised
Learning [50.24174211654775]
We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks.
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair.
A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
arXiv Detail & Related papers (2021-02-27T03:17:20Z) - Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client.
Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation.
This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
arXiv Detail & Related papers (2021-02-14T05:36:25Z) - Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN)
GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
arXiv Detail & Related papers (2020-06-23T04:13:23Z) - Understanding the Limitations of Network Online Learning [5.925292989496618]
We investigate limitations of learning to complete partially observed networks via node querying.
We call this querying process Network Online Learning and present a family of algorithms called NOL*.
arXiv Detail & Related papers (2020-01-09T13:59:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.