Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Authors: Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxin Ning, Kunfeng Lai, Philip S. Yu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. |
| Researcher Affiliation | Collaboration | 1 Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University 2 State Key Laboratory of Software Development Environment, Beihang University 3 Department of Computer Science and Engineering, Hong Kong University of Science and Technology 4 Platform and Content Group, Tencent 5 Institute for Data Science, Tsinghua University 6 Department of Computer Science, University of Illinois at Chicago |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of this work is publicly available at https://github.com/Ring BDStack/PPGCN. |
| Open Datasets | No | We select two independent social media platforms, news APP from Tencent (a popular APP for young people) and Sina Weibo (a hybrid of Twitter and Facebook, the Twitter of China and Chinese Social Media), to collect datasets. The paper does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for these datasets. |
| Dataset Splits | Yes | For both of the two datasets, we use 60% of samples as training set, 20% of samples as development set and the remaining 20% of as test set. |
| Hardware Specification | No | The paper mentions 'Tensorflow-gpu' indicating GPU usage, but does not specify exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications. |
| Software Dependencies | Yes | The operating system and software platforms are Ubuntu 5.4.0, Tensorflow-gpu (1.4.0) and Python 2.7. |
| Experiment Setup | Yes | We assume that if a pair of event instances ei and ej belongs to the same event classification, we name the pair ei and ej as a positivepair sample. If a pair of event instances ei and ej belongs to two different events classification, we name the pair ei and ej as a negative-pair sample. ... we first randomly select R (i.e., 1000) event instances as a preliminary set, then randomly select two event instances for each event instance in the set to form one positive-pair sample and one negative-pair sample, and finally we can construct a 2R event instance pairs set from training samples. Here, both the positive-pair and negative-pair samples are equal to R. Second, we randomly sample the B (i.e., 64) samples from the 2R (i.e., 2000) event instance pairs set to form a batch to forward propagation of our proposed model. Third, the second step is cycled E (i.e., 32) times to form an epoch. ...the coefficient c is 0.01 to avoid no upper bound output. ...we can use a cross entropy function as our model s loss function, and employ the popular stochastic gradient descent (SGD) method to iterate all parameters. |