Found 3685 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Jointly Learning Entity and Relation Representations for Entity Alignment

Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge

**graphs**(KGs). Expand abstract. Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge

**graphs**(KGs). Recent developments in the field often take an embedding-based approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. However, most existing works do not explicitly utilize useful relation representations to assist in entity alignment, which, as we will show in the paper, is a simple yet effective way for improving entity alignment. This paper presents a novel joint learning framework for entity alignment. At the core of our approach is a**Graph**Convolutional Network (GCN) based framework for learning both entity and relation representations. Rather than relying on pre-aligned relation seeds to learn relation representations, we first approximate them using entity embeddings learned by the GCN. We then incorporate the relation approximation into entities to iteratively learn better representations for both. Experiments performed on three real-world cross-lingual datasets show that our approach substantially outperforms state-of-the-art entity alignment methods.3 days ago

4/10 relevant

arXiv

4/10 relevant

arXiv

Concepts of signed **graph** coloring

The paper surveys some concepts of signed

**graph**colorings. Expand abstract. The paper surveys some concepts of signed

**graph**colorings.3 days ago

5/10 relevant

arXiv

5/10 relevant

arXiv

Representing split **graphs** by words

Moreover, we use split

**graph**s, and also provide an alternative solution, to show that gluing two word-representable**graphs**in any clique of size at least 2 may, or may not, result in a word-representable graph. Expand abstract. There is a long line of research in the literature dedicated to word-representable graphs, which generalize several important classes of

**graphs**. However, not much is known about word-representability of split graphs, another important class of**graphs**. In this paper, we show that threshold graphs, a subclass of split graphs, are word-representable. Further, we prove a number of general theorems on word-representable split graphs, and use them to characterize computationally such**graphs**with cliques of size 5 in terms of 9 forbidden subgraphs, thus extending the known characterization for word-representable split**graphs**with cliques of size 4. Moreover, we use split graphs, and also provide an alternative solution, to show that gluing two word-representable**graphs**in any clique of size at least 2 may, or may not, result in a word-representable**graph**. The two surprisingly simple solutions provided by us answer a question that was open for about ten years.3 days ago

9/10 relevant

arXiv

9/10 relevant

arXiv

Almost optimal classical approximation algorithms for a quantum generalization of Max-Cut

This model is notoriously difficult to solve exactly, even on bipartite

**graphs**, in stark contrast to the classical setting of Max-Cut. Expand abstract. Approximation algorithms for constraint satisfaction problems (CSPs) are a central direction of study in theoretical computer science. In this work, we study classical product state approximation algorithms for a physically motivated quantum generalization of Max-Cut, known as the quantum Heisenberg model. This model is notoriously difficult to solve exactly, even on bipartite graphs, in stark contrast to the classical setting of Max-Cut. Here we show, for any interaction graph, how to classically and efficiently obtain approximation ratios 0.649 (anti-ferromagnetic XY model) and 0.498 (anti-ferromagnetic Heisenberg XYZ model). These are almost optimal; we show that the best possible ratios achievable by a product state for these models is 2/3 and 1/2, respectively.

4 days ago

4/10 relevant

arXiv

4/10 relevant

arXiv

ASU at Text**Graphs** 2019 Shared Task: Explanation ReGeneration using
Language Models and Iterative Re-Ranking

The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large

**graphs**. Expand abstract. In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large

**graphs**. Our approach consists of modeling the explanation regeneration task as a \textit{learning to rank} problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative re-ranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3\% on the test set.4 days ago

4/10 relevant

arXiv

4/10 relevant

arXiv

Lower Bound for (Sum) Coloring Problem

We improve the lower bound for 18

**graphs**of standard benchmark DIMACS, and prove the optimal value for 4**graph**s by reaching their known upper bound. Expand abstract. The Minimum Sum Coloring Problem is a variant of the

**Graph**Vertex Coloring Problem, for which each color has a weight. This paper presents a new way to find a lower bound of this problem, based on a relaxation into an integer partition problem with additional constraints. We improve the lower bound for 18**graphs**of standard benchmark DIMACS, and prove the optimal value for 4**graphs**by reaching their known upper bound.4 days ago

6/10 relevant

arXiv

6/10 relevant

arXiv

Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

Domain-specific knowledge

**graphs**constructed from natural language text are ubiquitous in today's world. Expand abstract. Domain-specific knowledge

**graphs**constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge**graph**is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge**graph**completion, grounding, querying, etc. Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document, by finding model parameters which maximize the likelihood of generating the specific document over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.4 days ago

4/10 relevant

arXiv

4/10 relevant

arXiv

Moments of Uniform Random Multi**graphs** with Fixed Degree Sequences

We study the expected adjacency matrix of a uniformly random multigraph with fixed degree sequence $\mathbf{d}$. Expand abstract.

We study the expected adjacency matrix of a uniformly random multigraph with fixed degree sequence $\mathbf{d}$. This matrix arises in a variety of analyses of networked data sets, including modularity-maximization and mean-field theories of spreading processes. Its structure is well-understood for large, sparse, simple graphs: the expected number of edges between nodes $i$ and $j$ is roughly $\frac{d_id_j}{\sum_\ell{d_\ell}}$. Many network data sets are neither large, sparse, nor simple, and in these cases the approximation no longer applies. We derive a novel estimator using a dynamical approach: the estimator emerges from the stationarity conditions of a class of Markov Chain Monte Carlo algorithms for

**graph**sampling. Nonasymptotic error bounds are available under mild assumptions, and the estimator can be computed efficiently. We test the estimator on a small network, finding that it enjoys relative bias against ground truth a full order of magnitude smaller than the standard expression. We then compare modularity maximization techniques using both the standard and novel estimator, finding that the behavior of algorithms depends significantly on the estimator choice. Our results emphasize the importance of using carefully specified random**graph**models in data scientific applications.4 days ago

6/10 relevant

arXiv

6/10 relevant

arXiv

Detecting malicious logins as **graph** anomalies

The ability of the method to identify malicious logins among normal activity is tested with simulated

**graphs**of login activity representative of adversarial lateral movement. Expand abstract. Authenticated lateral movement via compromised accounts is a common adversarial maneuver that is challenging to discover with signature- or rules-based intrusion detection systems. In this work a behavior-based approach to detecting malicious logins to novel systems indicative of lateral movement is presented, in which a user's historical login activity is used to build a model of putative "normal" behavior. This historical login activity is represented as a collection of daily login graphs, which encode authentications among accessed systems. Each system, or

**graph**vertex, is described by a set of**graph**centrality measures that characterize it and the local topology of its login**graph**. The unsupervised technique of non-negative matrix factorization is then applied to this set of features to assign each vertex to a role that summarizes how the system participates in logins. The reconstruction error quantifying how well each vertex fits into its role is then computed, and the statistics of this error can be used to identify outlier vertices that correspond to systems involved in unusual logins. We test this technique with a small cohort of privileged accounts using real login data from an operational enterprise network. The ability of the method to identify malicious logins among normal activity is tested with simulated**graphs**of login activity representative of adversarial lateral movement. We find that the method is generally successful at detecting a broad range of lateral movement for each user, with false positive rates significantly lower than those resulting from alerts based solely on login novelty.4 days ago

7/10 relevant

arXiv

7/10 relevant

arXiv

A Characterization of Circle **Graphs** in Terms of Total Unimodularity

A graph $G$ has an associated multimatroid $\mathcal{Z}_3(G)$, which is equivalent to the isotropic system of $G$ studied by Bouchet. Expand abstract.

A

**graph**$G$ has an associated multimatroid $\mathcal{Z}_3(G)$, which is equivalent to the isotropic system of $G$ studied by Bouchet. In previous work it was shown that $G$ is a circle**graph**if and only if for every field $\mathbb F$, the rank function of $\mathcal{Z}_3(G)$ can be extended to the rank function of an $\mathbb F$-representable matroid. In the present paper we strengthen this result using a multimatroid analogue of total unimodularity. As a consequence we obtain a characterization of matroid planarity in terms of this total-unimodularity analogue.4 days ago

7/10 relevant

arXiv

7/10 relevant

arXiv