5 Search Results for "Biasotti, Silvia"

Document
DOI: 10.4230/LIPIcs.SoCG.2026.78
Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper

Authors: Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
The vast chemical space, encompassing virtually innumerable molecules and materials, presents both immense opportunities and significant challenges. The design and discovery of novel drugs and functional materials may be viewed as a search within this space; however, the sheer scale of potential candidates renders exhaustive exploration infeasible. To address this, we introduce Chemical Mapper, a framework that integrates topological data analysis with deep learning to enable the visual exploration and analysis of chemical latent spaces. At its core, Chemical Mapper employs mapper, a widely used tool in topological data analysis, to investigate the organizational principles of chemical latent spaces defined by molecular representations learned by geometric deep learning models. In doing so, Chemical Mapper not only highlights groups of molecular representations but also uncovers the relationships among them through linkages and branching structures. Our results show that Chemical Mapper reveals intrinsic patterns associated with molecular scaffolds, functional groups, and chemical properties, as well as the structural and functional evolutions of the molecules.
Cite as

Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang. Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 78:1-78:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{meduri_et_al:LIPIcs.SoCG.2026.78,
  author =	{Meduri, Dhruv and Hu, Chuan-Shen and Shen, Cong and Xia, Kelin and Wang, Bei},
  title =	{{Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{78:1--78:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.78},
  URN =		{urn:nbn:de:0030-drops-258854},
  doi =		{10.4230/LIPIcs.SoCG.2026.78},
  annote =	{Keywords: Practice of computational topology, topological data analysis, applications in chemistry, mapper algorithm, high-dimensional data analysis, chemical spaces, geometric deep learning, latent space geometry}
}
Document
Poster Abstract
DOI: 10.4230/LIPIcs.GD.2025.50
Reeb Lobsters Are 1-Planar (Poster Abstract)

Authors: Maarten Löffler, Miriam Münch, and Ignaz Rutter

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
Very recently, Chambers, Fasy, Hosseini Sereshgi and Löffler [Erin W. Chambers et al., 2025] showed that every Reeb caterpillar admits a crossing-free drawing. It turns out that this does not hold for Reeb lobsters but we show that these graphs admit drawings with at most one crossing per edge.
Cite as

Maarten Löffler, Miriam Münch, and Ignaz Rutter. Reeb Lobsters Are 1-Planar (Poster Abstract). In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 50:1-50:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{loffler_et_al:LIPIcs.GD.2025.50,
  author =	{L\"{o}ffler, Maarten and M\"{u}nch, Miriam and Rutter, Ignaz},
  title =	{{Reeb Lobsters Are 1-Planar}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{50:1--50:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.50},
  URN =		{urn:nbn:de:0030-drops-250365},
  doi =		{10.4230/LIPIcs.GD.2025.50},
  annote =	{Keywords: Reeb graphs, layered drawings, local crossing number}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2025.41
Decomposing Multiparameter Persistence Modules

Authors: Tamal K. Dey, Jan Jendrysiak, and Michael Kerber

Published in: LIPIcs, Volume 332, 41st International Symposium on Computational Geometry (SoCG 2025)

Abstract
Dey and Xin (J.Appl.Comput.Top., 2022) describe an algorithm to decompose finitely presented multiparameter persistence modules using a matrix reduction algorithm. Their algorithm only works for modules whose generators and relations are distinctly graded. We extend their approach to work on all finitely presented modules and introduce several improvements that lead to significant speed-ups in practice. Our algorithm is fixed-parameter tractable with respect to the maximal number of relations of the same degree and with further optimisation we obtain an O(n³) time algorithm for interval-decomposable modules. In particular, we can decide interval-decomposability in this time. As a by-product to the proofs of correctness we develop a theory of parameter restriction for persistence modules. Our algorithm is implemented as a software library aida, the first to enable the decomposition of large inputs. We show its capabilities via extensive experimental evaluation.
Cite as

Tamal K. Dey, Jan Jendrysiak, and Michael Kerber. Decomposing Multiparameter Persistence Modules. In 41st International Symposium on Computational Geometry (SoCG 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 332, pp. 41:1-41:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dey_et_al:LIPIcs.SoCG.2025.41,
  author =	{Dey, Tamal K. and Jendrysiak, Jan and Kerber, Michael},
  title =	{{Decomposing Multiparameter Persistence Modules}},
  booktitle =	{41st International Symposium on Computational Geometry (SoCG 2025)},
  pages =	{41:1--41:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-370-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{332},
  editor =	{Aichholzer, Oswin and Wang, Haitao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2025.41},
  URN =		{urn:nbn:de:0030-drops-231939},
  doi =		{10.4230/LIPIcs.SoCG.2025.41},
  annote =	{Keywords: Topological Data Analysis, Multiparameter Persistence Modules, Persistence, Decomposition}
}
Document
DOI: 10.4230/DagSemProc.06171.7
Partial Matching by Structural Descriptors

Authors: Simone Marini, Biasotti Silvia, and Falcidieno Bianca

Published in: Dagstuhl Seminar Proceedings, Volume 6171, Content-Based Retrieval (2006)

Abstract
The extended abstract describes a method for recognizing similar sub-parts of objects described by 3D polygonal meshes. The innovation of this method is the coupling of structure and geometry in the matching process. First of all, the structure of the shape is coded in a graph where each node is associated to a sub-part of the shape. Then, the matching between two shapes is approached using a graph-matching technique relying upon a geometric description of each sub-part.
Cite as

Simone Marini, Biasotti Silvia, and Falcidieno Bianca. Partial Matching by Structural Descriptors. In Content-Based Retrieval. Dagstuhl Seminar Proceedings, Volume 6171, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{marini_et_al:DagSemProc.06171.7,
  author =	{Marini, Simone and Silvia, Biasotti and Bianca, Falcidieno},
  title =	{{Partial Matching by Structural Descriptors}},
  booktitle =	{Content-Based Retrieval},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6171},
  editor =	{Tim Crawford and Remco C. Veltkamp},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06171.7},
  URN =		{urn:nbn:de:0030-drops-6511},
  doi =		{10.4230/DagSemProc.06171.7},
  annote =	{Keywords: Partial Matching, 3D Structural Shape Descriptor, Graph Matching}
}
Document
DOI: 10.4230/DagSemProc.06171.10
Structural Descriptors for 3D Shapes

Authors: Michela Spagnuolo, Silvia Biasotti, Bianca Falcidieno, and Simone Marini

Published in: Dagstuhl Seminar Proceedings, Volume 6171, Content-Based Retrieval (2006)

Abstract
Assessing the similarity among 3D shapes is a very complex and challenging research topic. While human perception have been widely studied and produced theories that received a large consensus, the computational aspects of 3D shape retrieval and matching have been only recently addressed. The majority of the methods proposed in the literature mainly focus on the geometry of shapes, in the sense of considering its spatial distribution or extent in the 3D space. From a practical point of view, the main advantage of these methods is that they do not make specific assumption on the topology of the digital models, usually triangle meshes or even triangle soups. Moreover, these methods are also computationally efficient. There is a growing consensus, however, that shapes are recognized and coded mentally in terms of relevant parts and their spatial configuration, or structure. Methods approaching the problem from a geometric point of view do not take into account the structure of the shape and generally the similarity distance between two objects depends on their spatial embedding. The presentation will discuss the definition and use of structural descriptions for assessing shape similarity. The idea is to define a shape description framework based on results of differential topology which deal with the description of shapes by means of the properties of one, or more, real-valued functions defined over the shape. Studying these properties, several topological descriptions of the shape can be defined, which may also encode different geometric and morphological attributes that globally and locally describe the shape. Examples and results will be discussed and ongoing work outlined. This work is partially supported by the EU Newtwork of Excellence AIM{@}SHAPE.
Cite as

Michela Spagnuolo, Silvia Biasotti, Bianca Falcidieno, and Simone Marini. Structural Descriptors for 3D Shapes. In Content-Based Retrieval. Dagstuhl Seminar Proceedings, Volume 6171, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{spagnuolo_et_al:DagSemProc.06171.10,
  author =	{Spagnuolo, Michela and Biasotti, Silvia and Falcidieno, Bianca and Marini, Simone},
  title =	{{Structural Descriptors for 3D Shapes}},
  booktitle =	{Content-Based Retrieval},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6171},
  editor =	{Tim Crawford and Remco C. Veltkamp},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06171.10},
  URN =		{urn:nbn:de:0030-drops-6532},
  doi =		{10.4230/DagSemProc.06171.10},
  annote =	{Keywords: 3D shape descriptors, computational topology}
}
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