8,317 research outputs found
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
Parallel Toolkit for Measuring the Quality of Network Community Structure
Many networks display community structure which identifies groups of nodes
within which connections are denser than between them. Detecting and
characterizing such community structure, which is known as community detection,
is one of the fundamental issues in the study of network systems. It has
received a considerable attention in the last years. Numerous techniques have
been developed for both efficient and effective community detection. Among
them, the most efficient algorithm is the label propagation algorithm whose
computational complexity is O(|E|). Although it is linear in the number of
edges, the running time is still too long for very large networks, creating the
need for parallel community detection. Also, computing community quality
metrics for community structure is computationally expensive both with and
without ground truth. However, to date we are not aware of any effort to
introduce parallelism for this problem. In this paper, we provide a parallel
toolkit to calculate the values of such metrics. We evaluate the parallel
algorithms on both distributed memory machine and shared memory machine. The
experimental results show that they yield a significant performance gain over
sequential execution in terms of total running time, speedup, and efficiency.Comment: 8 pages; in Network Intelligence Conference (ENIC), 2014 Europea
Fast Detection of Community Structures using Graph Traversal in Social Networks
Finding community structures in social networks is considered to be a
challenging task as many of the proposed algorithms are computationally
expensive and does not scale well for large graphs. Most of the community
detection algorithms proposed till date are unsuitable for applications that
would require detection of communities in real-time, especially for massive
networks. The Louvain method, which uses modularity maximization to detect
clusters, is usually considered to be one of the fastest community detection
algorithms even without any provable bound on its running time. We propose a
novel graph traversal-based community detection framework, which not only runs
faster than the Louvain method but also generates clusters of better quality
for most of the benchmark datasets. We show that our algorithms run in O(|V | +
|E|) time to create an initial cover before using modularity maximization to
get the final cover.
Keywords - community detection; Influenced Neighbor Score; brokers; community
nodes; communitiesComment: 29 pages, 9 tables, and 13 figures. Accepted in "Knowledge and
Information Systems", 201
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