Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminologyComment: 28 pages, 9 figures, 8 table

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Self-supervised Heterogeneous Graph Variational Autoencoders

Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existing heterogeneous graph neural networks (HGNNs) ignore the problems of missing attributes, inaccurate attributes and scarce labels for nodes, which limits their expressiveness. In this paper, we propose a generative self-supervised model SHAVA to address these issues simultaneously. Specifically, SHAVA first initializes all the nodes in the graph with a low-dimensional representation matrix. After that, based on the variational graph autoencoder framework, SHAVA learns both node-level and attribute-level embeddings in the encoder, which can provide fine-grained semantic information to construct node attributes. In the decoder, SHAVA reconstructs both links and attributes. Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes. In this way, SHAVA can not only complete informative features for non-attributed nodes, but rectify inaccurate ones for attributed nodes. Finally, we conduct extensive experiments to show the superiority of SHAVA in tackling HINs with missing and inaccurate attributes

Deep Learning of Representations: Looking Forward

Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges

MANIFOLD REPRESENTATIONS OF MUSICAL SIGNALS AND GENERATIVE SPACES

Tra i diversi campi di ricerca nell\u2019ambito dell\u2019informatica musicale, la sintesi e la generazione di segnali audio incarna la pluridisciplinalita\u300 di questo settore, nutrendo insieme le pratiche scientifiche e musicale dalla sua creazione. Inerente all\u2019informatica dalla sua creazione, la generazione audio ha ispirato numerosi approcci, evolvendo colle pratiche musicale e gli progressi tecnologici e scientifici. Inoltre, alcuni processi di sintesi permettono anche il processo inverso, denominato analisi, in modo che i parametri di sintesi possono anche essere parzialmente o totalmente estratti dai suoni, dando una rappresentazione alternativa ai segnali analizzati. Per di piu\u300, la recente ascesa dei algoritmi di l\u2019apprendimento automatico ha vivamente interrogato il settore della ricerca scientifica, fornendo potenti data-centered metodi che sollevavano diversi epistemologici interrogativi, nonostante i sui efficacia. Particolarmente, un tipo di metodi di apprendimento automatico, denominati modelli generativi, si concentrano sulla generazione di contenuto originale usando le caratteristiche che hanno estratti dei dati analizzati. In tal caso, questi modelli non hanno soltanto interrogato i precedenti metodi di generazione, ma anche sul modo di integrare questi algoritmi nelle pratiche artistiche. Mentre questi metodi sono progressivamente introdotti nel settore del trattamento delle immagini, la loro applicazione per la sintesi di segnali audio e ancora molto marginale. In questo lavoro, il nostro obiettivo e di proporre un nuovo metodo di audio sintesi basato su questi nuovi tipi di generativi modelli, rafforazti dalle nuove avanzati dell\u2019apprendimento automatico. Al primo posto, facciamo una revisione dei approcci esistenti nei settori dei sistemi generativi e di sintesi sonore, focalizzando sul posto di nostro lavoro rispetto a questi disciplini e che cosa possiamo aspettare di questa collazione. In seguito, studiamo in maniera piu\u300 precisa i modelli generativi, e come possiamo utilizzare questi recenti avanzati per l\u2019apprendimento di complesse distribuzione di suoni, in un modo che sia flessibile e nel flusso creativo del utente. Quindi proponiamo un processo di inferenza / generazione, il quale rifletta i processi di analisi/sintesi che sono molto usati nel settore del trattamento del segnale audio, usando modelli latenti, che sono basati sull\u2019utilizzazione di un spazio continuato di alto livello, che usiamo per controllare la generazione. Studiamo dapprima i risultati preliminari ottenuti con informazione spettrale estratte da diversi tipi di dati, che valutiamo qualitativamente e quantitativamente. Successiva- mente, studiamo come fare per rendere questi metodi piu\u300 adattati ai segnali audio, fronteggiando tre diversi aspetti. Primo, proponiamo due diversi metodi di regolarizzazione di questo generativo spazio che sono specificamente sviluppati per l\u2019audio : una strategia basata sulla traduzione segnali / simboli, e una basata su vincoli percettivi. Poi, proponiamo diversi metodi per fronteggiare il aspetto temporale dei segnali audio, basati sull\u2019estrazione di rappresentazioni multiscala e sulla predizione, che permettono ai generativi spazi ottenuti di anche modellare l\u2019aspetto dinamico di questi segnali. Per finire, cambiamo il nostro approccio scientifico per un punto di visto piu\u301 ispirato dall\u2019idea di ricerca e creazione. Primo, descriviamo l\u2019architettura e il design della nostra libreria open-source, vsacids, sviluppata per permettere a esperti o non-esperti musicisti di provare questi nuovi metodi di sintesi. Poi, proponiamo una prima utilizzazione del nostro modello con la creazione di una performance in real- time, chiamata \ue6go, basata insieme sulla nostra libreria vsacids e sull\u2019uso di une agente di esplorazione, imparando con rinforzo nel corso della composizione. Finalmente, tramo dal lavoro presentato alcuni conclusioni sui diversi modi di migliorare e rinforzare il metodo di sintesi proposto, nonche\u301 eventuale applicazione artistiche.Among the diverse research fields within computer music, synthesis and generation of audio signals epitomize the cross-disciplinarity of this domain, jointly nourishing both scientific and artistic practices since its creation. Inherent in computer music since its genesis, audio generation has inspired numerous approaches, evolving both with musical practices and scientific/technical advances. Moreover, some syn- thesis processes also naturally handle the reverse process, named analysis, such that synthesis parameters can also be partially or totally extracted from actual sounds, and providing an alternative representation of the analyzed audio signals. On top of that, the recent rise of machine learning algorithms earnestly questioned the field of scientific research, bringing powerful data-centred methods that raised several epistemological questions amongst researchers, in spite of their efficiency. Especially, a family of machine learning methods, called generative models, are focused on the generation of original content using features extracted from an existing dataset. In that case, such methods not only questioned previous approaches in generation, but also the way of integrating this methods into existing creative processes. While these new generative frameworks are progressively introduced in the domain of image generation, the application of such generative techniques in audio synthesis is still marginal. In this work, we aim to propose a new audio analysis-synthesis framework based on these modern generative models, enhanced by recent advances in machine learning. We first review existing approaches, both in sound synthesis and in generative machine learning, and focus on how our work inserts itself in both practices and what can be expected from their collation. Subsequently, we focus a little more on generative models, and how modern advances in the domain can be exploited to allow us learning complex sound distributions, while being sufficiently flexible to be integrated in the creative flow of the user. We then propose an inference / generation process, mirroring analysis/synthesis paradigms that are natural in the audio processing domain, using latent models that are based on a continuous higher-level space, that we use to control the generation. We first provide preliminary results of our method applied on spectral information, extracted from several datasets, and evaluate both qualitatively and quantitatively the obtained results. Subsequently, we study how to make these methods more suitable for learning audio data, tackling successively three different aspects. First, we propose two different latent regularization strategies specifically designed for audio, based on and signal / symbol translation and perceptual constraints. Then, we propose different methods to address the inner temporality of musical signals, based on the extraction of multi-scale representations and on prediction, that allow the obtained generative spaces that also model the dynamics of the signal. As a last chapter, we swap our scientific approach to a more research & creation-oriented point of view: first, we describe the architecture and the design of our open-source library, vsacids, aiming to be used by expert and non-expert music makers as an integrated creation tool. Then, we propose an first musical use of our system by the creation of a real-time performance, called aego, based jointly on our framework vsacids and an explorative agent using reinforcement learning to be trained during the performance. Finally, we draw some conclusions on the different manners to improve and reinforce the proposed generation method, as well as possible further creative applications.A\u300 travers les diffe\u301rents domaines de recherche de la musique computationnelle, l\u2019analysie et la ge\u301ne\u301ration de signaux audio sont l\u2019exemple parfait de la trans-disciplinarite\u301 de ce domaine, nourrissant simultane\u301ment les pratiques scientifiques et artistiques depuis leur cre\u301ation. Inte\u301gre\u301e a\u300 la musique computationnelle depuis sa cre\u301ation, la synthe\u300se sonore a inspire\u301 de nombreuses approches musicales et scientifiques, e\u301voluant de pair avec les pratiques musicales et les avance\u301es technologiques et scientifiques de son temps. De plus, certaines me\u301thodes de synthe\u300se sonore permettent aussi le processus inverse, appele\u301 analyse, de sorte que les parame\u300tres de synthe\u300se d\u2019un certain ge\u301ne\u301rateur peuvent e\u302tre en partie ou entie\u300rement obtenus a\u300 partir de sons donne\u301s, pouvant ainsi e\u302tre conside\u301re\u301s comme une repre\u301sentation alternative des signaux analyse\u301s. Paralle\u300lement, l\u2019inte\u301re\u302t croissant souleve\u301 par les algorithmes d\u2019apprentissage automatique a vivement questionne\u301 le monde scientifique, apportant de puissantes me\u301thodes d\u2019analyse de donne\u301es suscitant de nombreux questionnements e\u301piste\u301mologiques chez les chercheurs, en de\u301pit de leur effectivite\u301 pratique. En particulier, une famille de me\u301thodes d\u2019apprentissage automatique, nomme\u301e mode\u300les ge\u301ne\u301ratifs, s\u2019inte\u301ressent a\u300 la ge\u301ne\u301ration de contenus originaux a\u300 partir de caracte\u301ristiques extraites directement des donne\u301es analyse\u301es. Ces me\u301thodes n\u2019interrogent pas seulement les approches pre\u301ce\u301dentes, mais aussi sur l\u2019inte\u301gration de ces nouvelles me\u301thodes dans les processus cre\u301atifs existants. Pourtant, alors que ces nouveaux processus ge\u301ne\u301ratifs sont progressivement inte\u301gre\u301s dans le domaine la ge\u301ne\u301ration d\u2019image, l\u2019application de ces techniques en synthe\u300se audio reste marginale. Dans cette the\u300se, nous proposons une nouvelle me\u301thode d\u2019analyse-synthe\u300se base\u301s sur ces derniers mode\u300les ge\u301ne\u301ratifs, depuis renforce\u301s par les avance\u301es modernes dans le domaine de l\u2019apprentissage automatique. Dans un premier temps, nous examinerons les approches existantes dans le domaine des syste\u300mes ge\u301ne\u301ratifs, sur comment notre travail peut s\u2019inse\u301rer dans les pratiques de synthe\u300se sonore existantes, et que peut-on espe\u301rer de l\u2019hybridation de ces deux approches. Ensuite, nous nous focaliserons plus pre\u301cise\u301ment sur comment les re\u301centes avance\u301es accomplies dans ce domaine dans ce domaine peuvent e\u302tre exploite\u301es pour l\u2019apprentissage de distributions sonores complexes, tout en e\u301tant suffisamment flexibles pour e\u302tre inte\u301gre\u301es dans le processus cre\u301atif de l\u2019utilisateur. Nous proposons donc un processus d\u2019infe\u301rence / g\ue9n\ue9ration, refle\u301tant les paradigmes d\u2019analyse-synthe\u300se existant dans le domaine de ge\u301ne\u301ration audio, base\u301 sur l\u2019usage de mode\u300les latents continus que l\u2019on peut utiliser pour contro\u302ler la ge\u301ne\u301ration. Pour ce faire, nous e\u301tudierons de\u301ja\u300 les re\u301sultats pre\u301liminaires obtenus par cette me\u301thode sur l\u2019apprentissage de distributions spectrales, prises d\u2019ensembles de donne\u301es diversifie\u301s, en adoptant une approche a\u300 la fois quantitative et qualitative. Ensuite, nous proposerons d\u2019ame\u301liorer ces me\u301thodes de manie\u300re spe\u301cifique a\u300 l\u2019audio sur trois aspects distincts. D\u2019abord, nous proposons deux strate\u301gies de re\u301gularisation diffe\u301rentes pour l\u2019analyse de signaux audio : une base\u301e sur la traduction signal/ symbole, ainsi qu\u2019une autre base\u301e sur des contraintes perceptives. Nous passerons par la suite a\u300 la dimension temporelle de ces signaux audio, proposant de nouvelles me\u301thodes base\u301es sur l\u2019extraction de repre\u301sentations temporelles multi-e\u301chelle et sur une ta\u302che supple\u301mentaire de pre\u301diction, permettant la mode\u301lisation de caracte\u301ristiques dynamiques par les espaces ge\u301ne\u301ratifs obtenus. En dernier lieu, nous passerons d\u2019une approche scientifique a\u300 une approche plus oriente\u301e vers un point de vue recherche & cre\u301ation. Premie\u300rement, nous pre\u301senterons notre librairie open-source, vsacids, visant a\u300 e\u302tre employe\u301e par des cre\u301ateurs experts et non-experts comme un outil inte\u301gre\u301. Ensuite, nous proposons une premie\u300re utilisation musicale de notre syste\u300me par la cre\u301ation d\u2019une performance temps re\u301el, nomme\u301e \ue6go, base\u301e a\u300 la fois sur notre librarie et sur un agent d\u2019exploration appris dynamiquement par renforcement au cours de la performance. Enfin, nous tirons les conclusions du travail accompli jusqu\u2019a\u300 maintenant, concernant les possibles ame\u301liorations et de\u301veloppements de la me\u301thode de synthe\u300se propose\u301e, ainsi que sur de possibles applications cre\u301atives