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Community Search: Learn from Small Data | | Shuheng Fang
; Kangfei Zhao
; Guanghua Li
; Jeffery Xu Yu
; | | Date: |
2 Jan 2022 | | Abstract: | Community Search (CS) is one of the fundamental graph analysis tasks, which
is a building block of various real applications. Given any query nodes, CS
aims to find cohesive subgraphs that query nodes belong to. Recently, a large
number of CS algorithms are designed. These algorithms adopt pre-defined
subgraph patterns to model the communities, which cannot find communities that
do not have such pre-defined patterns in real-world graphs. Thereby, machine
learning based approaches are proposed to capture flexible community structures
by learning from community ground-truth in a data-driven fashion. However,
existing approaches rely on sufficient training data to provide enough
generalization for machine learning models. In this paper, we study ML-based
approaches for community search, under the circumstance that the training data
is scarce. To learn from small data, we extract prior knowledge which is shared
across different graphs as CS tasks in advance. Subsequent small training data
from a new CS task are combined with the learned prior knowledge to help the
model well adapt to that specific task. A novel meta-learning based framework,
called CGNP, is designed and implemented to fulfill this learning procedure. A
meta CGNP model is a task-common node embedding function for clustering by
nature, learned by metric-based learning. To the best of our knowledge, CGNP is
the first meta model solution for CS. We compare CGNP with traditional CS
algorithms, e.g., CTC, ATC, ACQ, and ML baselines on real graph datasets with
ground-truth. Our experiments show that CGNP outperforms the native graph
algorithms and ML baselines 147% and 113% on F1-score by average. | | Source: | arXiv, 2201.00288 | | Services: | Forum | Review | PDF | Favorites | |
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