10 research outputs found
Principal Patterns on Graphs: Discovering Coherent Structures in Datasets
Graphs are now ubiquitous in almost every field of research. Recently, new
research areas devoted to the analysis of graphs and data associated to their
vertices have emerged. Focusing on dynamical processes, we propose a fast,
robust and scalable framework for retrieving and analyzing recurring patterns
of activity on graphs. Our method relies on a novel type of multilayer graph
that encodes the spreading or propagation of events between successive time
steps. We demonstrate the versatility of our method by applying it on three
different real-world examples. Firstly, we study how rumor spreads on a social
network. Secondly, we reveal congestion patterns of pedestrians in a train
station. Finally, we show how patterns of audio playlists can be used in a
recommender system. In each example, relevant information previously hidden in
the data is extracted in a very efficient manner, emphasizing the scalability
of our method. With a parallel implementation scaling linearly with the size of
the dataset, our framework easily handles millions of nodes on a single
commodity server
FMA: A Dataset For Music Analysis
We introduce the Free Music Archive (FMA), an open and easily accessible
dataset suitable for evaluating several tasks in MIR, a field concerned with
browsing, searching, and organizing large music collections. The community's
growing interest in feature and end-to-end learning is however restrained by
the limited availability of large audio datasets. The FMA aims to overcome this
hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio
from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a
hierarchical taxonomy of 161 genres. It provides full-length and high-quality
audio, pre-computed features, together with track- and user-level metadata,
tags, and free-form text such as biographies. We here describe the dataset and
how it was created, propose a train/validation/test split and three subsets,
discuss some suitable MIR tasks, and evaluate some baselines for genre
recognition. Code, data, and usage examples are available at
https://github.com/mdeff/fmaComment: ISMIR 2017 camera-read
Anomaly detection in the dynamics of web and social networks
In this work, we propose a new, fast and scalable method for anomaly
detection in large time-evolving graphs. It may be a static graph with dynamic
node attributes (e.g. time-series), or a graph evolving in time, such as a
temporal network. We define an anomaly as a localized increase in temporal
activity in a cluster of nodes. The algorithm is unsupervised. It is able to
detect and track anomalous activity in a dynamic network despite the noise from
multiple interfering sources. We use the Hopfield network model of memory to
combine the graph and time information. We show that anomalies can be spotted
with a good precision using a memory network. The presented approach is
scalable and we provide a distributed implementation of the algorithm. To
demonstrate its efficiency, we apply it to two datasets: Enron Email dataset
and Wikipedia page views. We show that the anomalous spikes are triggered by
the real-world events that impact the network dynamics. Besides, the structure
of the clusters and the analysis of the time evolution associated with the
detected events reveals interesting facts on how humans interact, exchange and
search for information, opening the door to new quantitative studies on
collective and social behavior on large and dynamic datasets.Comment: The Web Conference 2019, 10 pages, 7 figure
Anomaly detection in the dynamics of web and social networks
In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources. We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm. To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets
System, device, and method for contextual knowledge retrieval and display
A method to extract patterns of activity from time series of data structured as a network of objects, the method comprising the steps of creating a base graph of similar or related items from a given corpus by comparing intrinsic features of the items, combining time varying data on nodes or edges of the base graph, creating dynamic activity patterns by connecting nodes of the base graph according to a measure of activity from the time series of data, and creating static activity patterns by folding the dynamic activity patterns according to identifiers of the nodes from the base graph
Investigating advanced transaction models for federated database systems
Transaction management in federated database systems is a very difficult problem. In the literature, there are a lot of proposals for transaction models in federated database systems. However, all these models base on different assumptions or are restricted to certain application scenarios. In this paper, we analyze the underlying basic transaction types in order to have a foundation for comparing these models and for investigating their applicability in federated database system environments. (orig.)SIGLEAvailable from TIB Hannover: RR 4487(1997,7) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman
A Lie-Group Adaptive Method to Identify the Radiative Coefficients in Parabolic Partial Differential Equations
In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources. We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm.To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets