A Graph Database of Yelp Dataset Challenge 2018 and Using Cypher for Basic Statistics and Graph Pattern Exploration

Kronmueller, Michael; Chang, Dar-Jen; Hu, Hanqing; Desoky, A.

doi:10.1109/isspit.2018.8642700

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…We took advantage of the temporal clustering property of social media data (i.e., the fact that many similar nodes and edges are created during bursty periods) to compress the graph to save ingestion time, and dynamically adjusted to buffer to control the CPU load. Our work sits in the middle of graph analytics research underlying many data science applications [1], [11], [14] who use small data sets, and graph database research that promotes in-database graph analytics [12] who do not consider streaming input. We view the graph stream ingestion problem discussed in this paper as a component of optimized ingestion control in the AWESOME polystore system [5], [6] where multiple streams of heterogeneous data can flow into a component DBMS managed under the polystore.…”

Section: Discussionmentioning

confidence: 99%

“…Metrics like these are the building blocks of more complex analytical measures developed by graph-centric research communities. In future work, we will materialize more of these temporally evolving properties and use them for the evolutionary analysis of the social media graph, community detection [1], [11], [14], and other graph analytics operations [12], which will benefit from our continuous computation of these "building block" measures.…”

Section: Discussionmentioning

confidence: 99%

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Ingesting High-Velocity Streaming Graphs from Social Media Sources

Dasgupta¹,

Bagchi²,

Gupta³

2019

Preprint

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Many data science applications like social network analysis use graphs as their primary form of data. However, acquiring graph-structured data from social media presents some interesting challenges. The first challenge is the high data velocity and bursty nature of the social media data. The second challenge is that the complex nature of the data makes the ingestion process expensive. If we want to store the streaming graph data in a graph database, we face a third challenge -the database is very often unable to sustain the ingestion of high-velocity, high-burst data. We have developed an adaptive buffering mechanism and a graph compression technique that effectively mitigates the problem. A novel aspect of our method is that the adaptive buffering algorithm uses the data rate, the data content as well as the CPU resources of the database machine to determine an optimal data ingestion mechanism. We further show that an ingestion-time graph-compression strategy improves the efficiency of the data ingestion into the database. We have verified the efficacy of our ingestion optimization strategy through extensive experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ingesting High-Velocity Streaming Graphs from Social Media Sources

Dasgupta¹,

Bagchi²,

Gupta³

2019

Preprint

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“…We evaluate the performance of our proposed method on five public datasets: MovieLens-1M (ML-1M) [23], Yelp2018 [24], Amazon Books, Gowalla, and Alibaba-iFashion [1]. These datasets vary in domain, scale, and density.…”

Section: Datasetsmentioning

confidence: 99%

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Cao,

Chen,

Hao

et al. 2023

Mathematics

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Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

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