76 research outputs found
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Higher-order Clustering and Pooling for Graph Neural Networks
Graph Neural Networks achieve state-of-the-art performance on a plethora of
graph classification tasks, especially due to pooling operators, which
aggregate learned node embeddings hierarchically into a final graph
representation. However, they are not only questioned by recent work showing on
par performance with random pooling, but also ignore completely higher-order
connectivity patterns. To tackle this issue, we propose HoscPool, a
clustering-based graph pooling operator that captures higher-order information
hierarchically, leading to richer graph representations. In fact, we learn a
probabilistic cluster assignment matrix end-to-end by minimising relaxed
formulations of motif spectral clustering in our objective function, and we
then extend it to a pooling operator. We evaluate HoscPool on graph
classification tasks and its clustering component on graphs with ground-truth
community structure, achieving best performance. Lastly, we provide a deep
empirical analysis of pooling operators' inner functioning.Comment: CIKM 202
: Random Walk Diffusion meets Hashing for Scalable Graph Embeddings
Learning node representations is a crucial task with a plethora of
interdisciplinary applications. Nevertheless, as the size of the networks
increases, most widely used models face computational challenges to scale to
large networks. While there is a recent effort towards designing algorithms
that solely deal with scalability issues, most of them behave poorly in terms
of accuracy on downstream tasks. In this paper, we aim at studying models that
balance the trade-off between efficiency and accuracy. In particular, we
propose , a scalable embedding model that
computes binary node representations.
exploits random walk diffusion probabilities via stable random projection
hashing, towards efficiently computing embeddings in the Hamming space. Our
extensive experimental evaluation on various graphs has demonstrated that the
proposed model achieves a good balance between accuracy and efficiency compared
to well-known baseline models on two downstream tasks
CORECLUSTER: A Degeneracy Based Graph Clustering Framework
International audienceGraph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present \textsc{CoreCluster}, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clustering algorithm. Our approach capitalizes on processing thegraph in a hierarchical manner provided by its core expansion sequence, anordered partition of the graph into different levels according to the -coredecomposition. Such a partition provides a way to process the graph inan incremental manner that preserves its clustering structure, whilemaking the execution of the chosen clustering algorithm much faster due to thesmaller size of the graph's partitions onto which the algorithm operates
Learning Graph Representations for Influence Maximization
As the field of machine learning for combinatorial optimization advances,
traditional problems are resurfaced and readdressed through this new
perspective. The overwhelming majority of the literature focuses on small graph
problems, while several real-world problems are devoted to large graphs. Here,
we focus on two such problems: influence estimation, a #P-hard counting
problem, and influence maximization, an NP-hard problem. We develop GLIE, a
Graph Neural Network (GNN) that inherently parameterizes an upper bound of
influence estimation and train it on small simulated graphs. Experiments show
that GLIE provides accurate influence estimation for real graphs up to 10 times
larger than the train set. More importantly, it can be used for influence
maximization on considerably larger graphs, as the predictions ranking is not
affected by the drop of accuracy. We develop a version of CELF optimization
with GLIE instead of simulated influence estimation, surpassing the benchmark
for influence maximization, although with a computational overhead. To balance
the time complexity and quality of influence, we propose two different
approaches. The first is a Q-network that learns to choose seeds sequentially
using GLIE's predictions. The second defines a provably submodular function
based on GLIE's representations to rank nodes fast while building the seed set.
The latter provides the best combination of time efficiency and influence
spread, outperforming SOTA benchmarks.Comment: 2
Boosting Tricks for Word Mover's Distance
Due to the COVID-19 pandemic, the physical meeting of ICANN 2020 has been postponed. The event is scheduled next year’s ICANN in September 2021 in Bratislava, Slovakia.International audienceWord embeddings have opened a new path in creating novel approaches for addressing traditional problems in the natural language processing (NLP) domain. However, using word embeddings to compare text documents remains a relatively unexplored topic-with Word Mover's Distance (WMD) being the prominent tool used so far. In this paper, we present a variety of tools that can further improve the computation of distances between documents based on WMD. We demonstrate that, alternative stopwords, cross document-topic comparison, deep contextualized word vectors and convex metric learning, constitute powerful tools that can boost WMD
Time-varying Signals Recovery via Graph Neural Networks
The recovery of time-varying graph signals is a fundamental problem with
numerous applications in sensor networks and forecasting in time series.
Effectively capturing the spatio-temporal information in these signals is
essential for the downstream tasks. Previous studies have used the smoothness
of the temporal differences of such graph signals as an initial assumption.
Nevertheless, this smoothness assumption could result in a degradation of
performance in the corresponding application when the prior does not hold. In
this work, we relax the requirement of this hypothesis by including a learning
module. We propose a Time Graph Neural Network (TimeGNN) for the recovery of
time-varying graph signals. Our algorithm uses an encoder-decoder architecture
with a specialized loss composed of a mean squared error function and a Sobolev
smoothness operator.TimeGNN shows competitive performance against previous
methods in real datasets.Comment: Published in IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP) 2023, Greec
Fouille de Données dans les Réseaux Sociaux et d’Information : Dynamiques et Applications
Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and analysis methods constitute prominent tools. This dissertation contributes models, tools and observations to problems that arise in the area of mining social and information networks. We built upon computationally efficient graph mining methods in order to: (i) design models for analyzing the structure and dynamics of real-world networks towards unraveling properties that can further be used in practical applications; (ii) develop algorithmic tools for large-scale analytics on data with inherent (e.g., social networks) or without inherent (e.g., text) graph structure. In particular, for the former point we show how to model the engagement dynamics of large social networks and how to assess their vulnerability with respect to user departures from the network. In both cases, by unraveling the dynamics of real social networks, regularities and patterns about their structure and formation can be identified; such knowledge can further be used in various applications including churn prediction, anomaly detection and building robust social networking systems. For the latter, we examine how to identify influential users in complex networks, having direct applications to epidemic control and viral marketing and how to utilize graph mining techniques in order to enhance text analytics tasks and in particular the one of text categorization.Les réseaux (ou graphes) sont devenus omniprésents en raison de leur capacité à représenter naturellement les données trouvées dans de nombreuses et diverses disciplines. Extraire efficacement des informations pertinentes de graphes à grande échelle est un problème crucial et difficile ayant de nombreuses applications. À cette fin, les méthodes de fouille de données graphiques constituent des outils importants. Par l’introduction de nouveaux modèles et outils, et par la réalisation d’observations, cette thèse contribue à la résolution de problèmes qui se posent dans le domaine de la fouille de données provenant des réseaux sociaux et d'information. Nous utilisons des méthodes efficaces de fouille de données graphiques afin de: (i) concevoir des modèles pour l'analyse structurelle et dynamique des réseaux réels dans le but d’extraire des connaissances pouvant être utilisées en pratique, (ii) développer des outils algorithmiques pour l'analyse à grande échelle de données intrinsèquement graphiques (par exemple, réseaux sociaux) ou non intrinsèquement graphiques (par exemple, le texte). En particulier, pour le premier point, nous montrons comment modéliser la dynamique d'engagement au sein de grands réseaux sociaux et comment évaluer leur vulnérabilité par rapport aux départs des utilisateurs. Dans les deux cas, en mettant à jour la dynamique de réseaux sociaux réels, nous pouvons identifier des régularités et des motifs dans leur structure et leur formation. De telles connaissances trouvent de nombreuses applications comme la prévision du churn, la détection des anomalies et la construction de systèmes de réseautage robustes. Dans le deuxième point, nous nous concentrons sur l’identification des utilisateurs influents dans les réseaux complexes, avec des applications directes sur le contrôle des épidémies et le marketing viral, et sur l’utilisation de techniques de fouille de données graphiques pour améliorer les tâches d'analyse de texte et en particulier la classification du texte
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