Results 21 to 30 of about 75,062 (184)
Graph Traversal Edit Distance and Extensions
Journal of Computational Biology, 2020 Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned ...
Ali, Ebrahimpour Boroojeny +5 more
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Redefining the Graph Edit Distance [PDF]
SN Computer Science, 2021 AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs.
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IEEE Access, 2020 The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures.
Nadeem Iqbal Kajla +5 more
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An Exact Graph Edit Distance Algorithm for Solving Pattern Recognition Problems [PDF]
, 2015 International audienceGraph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining; it ...
Abu-Aisheh, Zeina +3 more
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Integrating species and interactions into similarity metrics: a graph theory-based approach to understanding community similarity [PDF]
PeerJ, 2019 Community similarity is often assessed through similarities in species occurrences and abundances (i.e., compositional similarity) or through the distribution of species interactions (i.e., interaction similarity).
Daniela N. López +3 more
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Efficient Top-k Graph Similarity Search With GED Constraints
IEEE Access, 2022 It is essential to identify similarity between graphs for various tasks in data mining, machine learning and pattern recognition. Graph edit distance (GED) is the most popular graph similarity measure thanks to its flexibility and versatility.
Jongik Kim
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Edit distance from graph spectra [PDF]
Proceedings Ninth IEEE International Conference on Computer Vision, 2003 We are concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that it lacks the formality and rigour of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that standard string edit distance techniques can be used ...
null Robles-Kelly, null Hancock
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Relative Hausdorff distance for network analysis
Applied Network Science, 2019 Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs.
Sinan G. Aksoy +3 more
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HGED: A Hybrid Search Algorithm for Efficient Parallel Graph Edit Distance Computation
IEEE Access, 2020 Graph edit distance (GED) is a measure for quantifying the similarity between two graphs. Because of its flexibility and versatility, GED is widely used in many real applications. However, the main disadvantage of GED is its high computational cost. Many
Jongik Kim
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A New Approach to Measuring the Similarity of Indoor Semantic Trajectories
ISPRS International Journal of Geo-Information, 2021 People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces.
Jin Zhu +5 more
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