2019 First International Conference on Graph Computing (GC)最新文献

Quantifying Outsourcing Risk Arising from Product Interdependencies in Supply Networks 供应网络中产品相互依赖导致的外包风险量化

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00019

A. Brintrup, Supun Perera

引用次数: 0

Short Paper: Graph Classification with Kernels, Embeddings and Convolutional Neural Networks 短文:用核、嵌入和卷积神经网络进行图分类

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00021

Monica Golahalli Seenappa, Katerina Potika, P. Potikas

{"title":"Short Paper: Graph Classification with Kernels, Embeddings and Convolutional Neural Networks","authors":"Monica Golahalli Seenappa, Katerina Potika, P. Potikas","doi":"10.1109/GC46384.2019.00021","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00021","url":null,"abstract":"In the graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches. We modify a kernel graph convolutional neural network approach, that extracts subgraphs (patches) from the graph using various community detection algorithms. These patches are provided as input to a graph kernel and max pooling is applied. We use different community detection algorithms and a shortest path graph kernel and compare their efficiency and performance. In this paper we compare three methods: a graph kernel, an embedding technique and one that uses convolutional neural networks by using eight real world datasets, ranging from biological to social networks.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122005858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching VEKG:用于复杂事件模式匹配的视频事件知识图

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00011

Piyush Yadav, E. Curry

{"title":"VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching","authors":"Piyush Yadav, E. Curry","doi":"10.1109/GC46384.2019.00011","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00011","url":null,"abstract":"Complex Event Processing (CEP) is a paradigm to detect event patterns over streaming data in a timely manner. Presently, CEP systems have inherent limitations to detect event patterns over video streams due to their data complexity and lack of structured data model. Modelling complex events in unstructured data like videos not only requires detecting objects but also the spatiotemporal relationships among objects. This work introduces a novel video representation technique where an input video stream is converted to a stream of graphs. We propose the Video Event Knowledge Graph (VEKG), a knowledge graph driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. To optimize the run-time system performance, we introduce a graph aggregation method VEKG-TAG, which provides an aggregated view of VEKG for a given time length. We defined a set of operators using event rules which can be used as a query and applied over VEKG graphs to discover complex video patterns. The system achieves an F-Score accuracy ranging between 0.75 to 0.86 for different patterns when queried over VEKG. In given experiments, pattern search time over VEKG-TAG was 2.3X faster as compared to the baseline.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131495087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

The Leaf Function for Graphs Associated with Penrose Tilings 与彭罗斯平铺相关联的图的叶函数

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00014

Carole Porrier, A. Massé

引用次数: 3

[Copyright notice] (版权)

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/gc46384.2019.00003

引用次数: 0

Combinatorial Text Classification: the Effect of Multi-Parameterized Correlation Clustering 组合文本分类:多参数化相关聚类的影响

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00013

Joseph R. Barr, Peter Shaw, F. Abu-Khzam, Jikang Chen

引用次数: 7

Visual Question Answering over Scene Graph 基于场景图的视觉问答

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00015

Soohyeong Lee, Ju-Whan Kim, Youngmin Oh, Joo Hyuk Jeon

{"title":"Visual Question Answering over Scene Graph","authors":"Soohyeong Lee, Ju-Whan Kim, Youngmin Oh, Joo Hyuk Jeon","doi":"10.1109/GC46384.2019.00015","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00015","url":null,"abstract":"Visual question answering (VQA) is a task that takes an image and a related natural language question as input, and produces an answer as output. A successful VQA algorithm requires two key components: to obtain a structured representation of an image and to process a natural language question on the structured representation. While traditional VQA tasks work on raw images or image segmentation, recent VQA datasets such as CLEVR and GQA provide scene graphs that capture objects and their relationships expressed inside an image. However, even when the ground-truth scene graph is given, it is non-trivial to get the right answer to a natural language question, as it needs a sophisticated algorithm to process the scene graph and the question together. We propose to encode a scene graph and a question using Graph Network (GN). Then, we feed the encoded graph with the question to the Memory, Attention, and Composition (MAC) model to classify the answer. By including the question as a global vector in GN, we achieved the accuracy of 96.3% in GQA, surpassing 83.5% of the baseline method reported by the authors of GQA, which also used MAC to classify the answer. Our work suggests that a context-based encoding of the scene graph is crucial for graph-based reasoning tasks such as graph-related question answering.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114501207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Using Embeddings for Dynamic Diverse Summarisation in Heterogeneous Graph Streams 基于嵌入的异构图流动态多样性摘要

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00010

Niki Pavlopoulou, E. Curry

{"title":"Using Embeddings for Dynamic Diverse Summarisation in Heterogeneous Graph Streams","authors":"Niki Pavlopoulou, E. Curry","doi":"10.1109/GC46384.2019.00010","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00010","url":null,"abstract":"A high-volume of data generated nowadays by the rise of Smart Cities and Internet of Things can be represented as graph streams. While many graph processing algorithms could analyse small graphs when challenging real-world graphs occur in distributed settings like sensor-based ones, a more suitable analysis is needed. Specifically, challenges like dynamism, heterogeneity, continuity and high-volume of these graph streams could benefit from real-time analysis. This analysis should happen with reduced network traffic and latency while maintaining high data expressibility and usability. Therefore, our key question is: Can we define a dynamic graph stream summarisation system that provides expressive graphs while ensuring high usability and limited resource usage? In this paper, we explore this question and propose a multi-source system with windowing, data fusion, conceptual clustering and top-k scoring that can result in expressive, dynamic graph summaries with limited resources at no expense of usability. Our results show that sending top-k fused diverse summarisation, results in 34% to 90% reduction of forwarded messages and redundancy-awareness with an F-score ranging from 0.57 to 0.88 depending on the k compared to sending all the available information. Also, the summaries' quality follows the agreement of ideal summaries determined by human judges. Nevertheless, these results occur at the expense of higher latency ranging from similar latency to the baseline up to 4 times more depending on the approach; therefore, there is some trade-off between latency, the number of forwarded messages, and expressiveness.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131210921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Investigative Graph Search using Graph Databases 使用图形数据库的调查图形搜索

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/GC46384.2019.00017

Shashika Ranga Muramudalige, Benjamin W. K. Hung, A. Jayasumana, I. Ray

{"title":"Investigative Graph Search using Graph Databases","authors":"Shashika Ranga Muramudalige, Benjamin W. K. Hung, A. Jayasumana, I. Ray","doi":"10.1109/GC46384.2019.00017","DOIUrl":"https://doi.org/10.1109/GC46384.2019.00017","url":null,"abstract":"Identification and tracking of individuals or groups perpetrating latent or emergent behaviors are significant in home-land security, cyber security, behavioral health, and consumer analytics. Graphs provide an effective formal mechanism to capture the relationships among individuals of interest as well as their behavior patterns. Graph databases, developed recently, serve as convenient data stores for such complex graphs and allow efficient retrievals via high-level libraries and the ability to implement custom queries. We introduce PINGS (Procedures for Investigative Graph Search) a graph database library of procedures for investigative search. We develop an inexact graph pattern matching technique and scoring mechanism within the database as custom procedures to identify latent behavioral patterns of individuals. It addresses, among other things, sub-graph isomorphism, an NP-hard problem, via an investigative search in graph databases. We demonstrate the capability of detecting such individuals and groups meeting query criteria using two data sets, a synthetically generated radicalization dataset and a publicly available crime dataset.","PeriodicalId":129268,"journal":{"name":"2019 First International Conference on Graph Computing (GC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

[Title page i] [标题页i]

2019 First International Conference on Graph Computing (GC) Pub Date : 2019-09-01 DOI: 10.1109/gc46384.2019.00001

引用次数: 0