Graph filter designs and implementations

The ability to model irregular data and the interactions between them have<br/>extended the traditional signal processing tools to the graph domain. Under<br/>these circumstances, the emergence of graph signal processing has offered a<br/>brand new framework for dealing with complex data. In particular, the graph<br/>Fourier transform (GFT) lets...

Graphon Filters: Graph Signal Processing in the Limit

Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about...

Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...

Topological Volterra Filters

To deal with high-dimensional data, graph filters have shown their power in both graph signal processing and data science. However, graph filters process signals exploiting only pairwise interactions between the nodes, and they are not able to exploit more complicated topological structures. Graph Volterra models, on the other hand, are also...

Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions

Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...

Privacy-Preserving Distributed Graph Filtering

With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools for processing sensitive data. As a step towards a solution, we...

Observing and tracking bandlimited graph processes from sampled measurements

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and...

Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes

Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model the observed networked data using graph filters. Therefore, in...

Graph-time signal processing: Filtering and sampling strategies

The necessity to process signals living in non-Euclidean domains, such as signals defined on the top of a graph, has led to the extension of signal processing techniques to the graph setting. Among different approaches, graph signal processing distinguishes itself by providing a Fourier analysis of these signals. Analogously to the Fourier...

Distributed Wiener-Based Reconstruction of Graph Signals

This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal...