Graph communities in Neo4j

Drakopoulos, Georgios; Gourgaris, Panagiotis; Kanavos, Andreas

doi:10.1007/s12530-018-9244-x

Cited by 20 publications

(7 citation statements)

References 29 publications

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“…Knowledge extraction technologies, such as data mining, natural language processing, and deep learning, are designed to extract entities and relations from unstructured industrial resources [31][32][33]. Researchers also enhance industrial knowledge graphs with generic knowledge graphs and exploit the storage of industrial knowledge graphs [34][35][36][37]. However, at present, industrial knowledge graphs are still entangled with the emerging resources, the knowledge mining of possible implicit relations between resources, and the collaboration of domain experts within the graph construction [38].…”

Section: Industrial Knowledge Graphmentioning

confidence: 99%

“…Open Information Extraction (OpenIE) annotator [59] is used to extract open-domain relation triples within structured and unstructured data. Neo4j JDBC driver [35] and RDF2Neo4j interpreter [60] are employed for data mapping, and attribute-based fusion [61] is used to fuse industrial resources from scattered relational databases in a unified paradigm. In such a paradigm, the entity set represents heterogeneous resources, and the edge set represents the relations among industrial resources.…”

Section: Industrial Knowledge Graph Constructionmentioning

confidence: 99%

“…The information and knowledge in resources are set as properties of the nodes. The industrial knowledge graph is then integrated and stored in the graph database Neo4j [35]. For KGE, entities and relations form triples that are embedded in a low-dimensional vector space using TransE [62].…”

Section: Industrial Knowledge Graph Constructionmentioning

confidence: 99%

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Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Liu

et al. 2022

Int J Comput Intell Syst

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Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.

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Section: Industrial Knowledge Graphmentioning

confidence: 99%

Section: Industrial Knowledge Graph Constructionmentioning

confidence: 99%

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Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Liu

et al. 2022

Int J Comput Intell Syst

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“…However, most of the existing coherence metrics are either prohibitively expensive, such as the maximum distance between vertices, or are prone to outliers, such as the diameter-based metrics [6,7]. In [5,16] are described implementations of established community discovery algorithms, namely the CNM over Neo4j, the Walktrap, the Louvain, and the Edge Betweeness or Newman-Girvan. To evaluate these algorithms efficiently, we rely mainly on how the graph partitioning obtained by such an algorithm translates into the functional Twitter domain and not on other structural criteria.…”

Section: Related Workmentioning

confidence: 99%

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Kanavos

Trigka

Δρίτσας

et al. 2021

Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops

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In social network analysis, it is crucial to discover a community through the retrospective decomposition of a large social graph into easily interpretable subgraphs. Four major community discovery algorithms, namely the Breadth-First Search, the Louvain, the MaxToMin, and the Propinquity Dynamics, are implemented. Their correctness was functionally evaluated in the four most widely used graphs with vastly different characteristics and a dataset retrieved from Twitter regarding cultural and natural heritage data because this platform reflects public perception about historical events through means such as advanced storytelling in users timelines. The primary finding was that the Propinquity Dynamics algorithm outperforms the other algorithms in terms of NMI for most graphs. In contrast, this algorithm with the Louvain performs almost the same regarding modularity.

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“…Moreover SOMs have been employed for a hierarchical clustering scheme for discovering latent gene expression patterns [25] and gene regulatory networks [26]. Given that the trained fuzzy cognitive maps can be represented as a fuzzy graph, clustering can be performed by fuzzy community discovery algorithms [27][28][29]. In Reference [30] fuzzy graphs have been used in a technique for estimating the number of clusters and their respective centroids.…”

Section: Previous Workmentioning

confidence: 99%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

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Graph communities in Neo4j

Cited by 20 publications

References 29 publications

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

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