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Network-based modeling of complex systems and data using the language of graphs has become an essential topic across a range of different disciplines. Arguably, this graph-based perspective derives its success from the relative simplicity…

Social and Information Networks · Computer Science 2023-08-11 Christian Bick , Elizabeth Gross , Heather A. Harrington , Michael T. Schaub

Partially ordered patterns (POPs) generalize the notion of classical patterns studied in the literature in the context of permutations, words, compositions and partitions. In this paper, we give a number of general, and specific enumerative…

Combinatorics · Mathematics 2022-04-20 Sergey Kitaev , Artem Pyatkin

An ordered graph is a graph with a total order over its vertices. A linear layout of an ordered graph is a partition of the edges into sets of either non-crossing edges, called stacks, or non-nesting edges, called queues. The stack (queue)…

Discrete Mathematics · Computer Science 2024-12-18 Deborah Haun , Laura Merker , Sergey Pupyrev

We introduce a new notation for representing labeled regular bipartite graphs of arbitrary degree. Several enumeration problems for labeled and unlabeled regular bipartite graphs have been introduced. A general algorithm for enumerating all…

Discrete Mathematics · Computer Science 2015-12-31 Vivek S. Nittoor

A total prime labeling of a graph of order $n$ is an extension of a prime labeling in which we distinctly label the vertices and edges. The goal of the labeling is for adjacent vertex labels to be relatively prime, and for each vertex of…

Combinatorics · Mathematics 2026-01-22 N. Bradley Fox , Joseph Spaeth

An ordering of the vertices of a graph is \emph{connected} if every vertex (but the first) has a neighbor among its predecessors. The greedy colouring algorithm of a graph with a connected order consists in taking the vertices in order, and…

Discrete Mathematics · Computer Science 2018-06-08 Ngoc Khang Le , Nicolas Trotignon

Abelian groups having partial orderings compatible with their binary operations have long been studied in the literature. In particular, lattice-ordered abelian groups constitute a universal-algebraic variety, and thus form a category which…

Rings and Algebras · Mathematics 2012-01-25 Elijah Stines

Analysis of higher-order organizations, usually small connected subgraphs called motifs, is a fundamental task on complex networks. This paper studies a new problem of testing higher-order clusterability: given query access to an undirected…

Data Structures and Algorithms · Computer Science 2023-10-09 Yifei Li , Donghua Yang , Jianzhong Li

In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated…

Machine Learning · Computer Science 2015-08-04 Tameem Adel , Alexander Wong , Daniel Stashuk

Deep networks are successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of…

Machine Learning · Computer Science 2018-12-05 Elad Hoffer , Nir Ailon

We investigate adjacency labeling schemes for graphs of bounded degree $\Delta = O(1)$. In particular, we present an optimal (up to an additive constant) $\log n + O(1)$ adjacency labeling scheme for bounded degree trees. The latter scheme…

Discrete Mathematics · Computer Science 2014-04-03 David Adjiashvili , Noy Rotbart

This paper outlines a method to determine whether two label-regular directed trees, are isomorphic and when they are almost isomorphic. The approach involves reinterpreting label-regular directed trees as universal covers of rooted graphs.…

Combinatorics · Mathematics 2023-03-13 Roman Gorazd

Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies. In this…

Machine Learning · Computer Science 2020-09-25 Wei-Hong Li , Chuan-Sheng Foo , Hakan Bilen

In this work we continue the study of non-chaotic asymptotic correlations in many element systems and discuss the emergence of a new notion of asymptotic correlation -- partial order -- in the Choose the Leader (CL) system. Similarly to the…

Mathematical Physics · Physics 2025-08-08 Amit Einav , Yue Jiang

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the…

Machine Learning · Computer Science 2021-06-18 Heitor Murilo Gomes , Maciej Grzenda , Rodrigo Mello , Jesse Read , Minh Huong Le Nguyen , Albert Bifet

This paper studies the structure of graphs with given tree-width and excluding a fixed complete bipartite subgraph, which generalises the bounded degree setting. We give a new structural description of such graphs in terms of so-called…

Combinatorics · Mathematics 2025-12-15 Chun-Hung Liu , David R. Wood

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with…

Machine Learning · Computer Science 2019-02-11 Lei Feng , Bo An

We show that under the proper forcing axiom the class of all Aronszajn lines behave like $\sigma$-scattered orders under the embeddability relation. In particular, we are able to show that the class of better quasi order labeled fragmented…

Logic · Mathematics 2020-03-30 Keegan Dasilva Barbosa

We show that a number of conditions on oriented graphs, all of which are satisfied with high probability by randomly oriented graphs, are equivalent. These equivalences are similar to those given by Chung, Graham and Wilson in the case of…

Combinatorics · Mathematics 2011-08-12 Simon Griffiths

Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on…

Computer Vision and Pattern Recognition · Computer Science 2019-09-20 Ahmet Iscen , Giorgos Tolias , Yannis Avrithis , Ondrej Chum