Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Behrouz, Ali"'
Autor:
Hashemi, Farnoosh, Behrouz, Ali
In many complex systems, the interactions between objects span multiple aspects. Multiplex networks are accurate paradigms to model such systems, where each edge is associated with a type. A key graph mining primitive is extracting dense subgraphs, a
Externí odkaz:
http://arxiv.org/abs/2406.13734
Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are classical approaches for univariate time series modeling due to their si
Externí odkaz:
http://arxiv.org/abs/2406.04320
Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however, exhibits quadratic time and space in input size, limiting their scal
Externí odkaz:
http://arxiv.org/abs/2403.19888
Autor:
Behrouz, Ali, Hashemi, Farnoosh
Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple layers. These methods, however, a
Externí odkaz:
http://arxiv.org/abs/2402.08678
Autor:
Behrouz, Ali, Hashemi, Farnoosh
Finding dense subgraphs of a large network is a fundamental problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applications over the last five decades. However, most existing studies hav
Externí odkaz:
http://arxiv.org/abs/2310.04893
Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-order interactions in complex systems. Representation learning for hypergraphs is essential for extracting patterns of the higher-order interactions that are critica
Externí odkaz:
http://arxiv.org/abs/2306.11147
Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The
Externí odkaz:
http://arxiv.org/abs/2303.08964
Autor:
Behrouz, Ali, Seltzer, Margo
The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic i
Externí odkaz:
http://arxiv.org/abs/2211.08378
Autor:
Behrouz, Ali, Hashemi, Farnoosh
Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely con
Externí odkaz:
http://arxiv.org/abs/2210.08811
Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for prediction, that work does not address policy design, because the algorithms
Externí odkaz:
http://arxiv.org/abs/2210.06825