5 accepted papers
Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a nove…
An important problem in graph signal processing (GSP) is to infer the topology of an unknown graph from a set of observations on the nodes of the graph, i.e. graph signals. Recently, graph learning (GL) approaches have been extended to learn dynamic graphs from temporal graph signals. However, exist…
Many real-world data are represented through the relations between data samples, i.e., a graph structure. Although many datasets come with a pre-existing graph, there is still a large number of applications where the graph structure is not readily available. An essential task for such cases is graph…
Graph learning (GL) aims to infer the topology of an unknown graph from a set of observations on its nodes, i.e., graph signals. While most of the existing GL approaches focus on homogeneous datasets, in many real world applications, data is heterogeneous, where graph signals are clustered and each…
Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the community structure, is an important problem in network science. Community detection aims to partition the network into…