New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics
Semi-supervised Community Detection;Clique;Annealing;Crystallization Kinetics
Yes
No ethics review needed.
1:Can you provide more intuitive examples or visual explanations of how crystallization kinetics translate to the community detection process? 

2:How does CLANN compare with simpler heuristic-based community detection methods in terms of performance and resource consumption?
1:The analogy with crystallization kinetics introduces a novel physics-grounded perspective for community detection, enriching the field with fresh conceptual insights.

2:Empirical evaluations show that CLANN outperforms established methods in both single and hybrid dataset scenarios, demonstrating its robustness and superior scalability.

3:The integration of the Nucleus Proposer and Transitive Annealer simplifies the growth process without relying on computationally heavy methods like GANs or reinforcement learning.

4:The paper provides extensive tests, including ablation studies and adaptability analyses, solidifying the validity of its contributions.
The manuscript presents a novel method for community detection called CLANN. This method addresses challenges in semi-supervised community detection, particularly issues with community core consistency and scalability. The authors draw an analogy between community formation and crystallization kinetics, introducing two main components: the Nucleus Proposer and the Transitive Annealer. These components use cliques as starting points and leverage physics-based principles to optimize and expand communities effectively. The paper claims that CLANN surpasses existing state-of-the-art methods in accuracy and efficiency based on extensive experiments across various datasets.
1:While the physics-based analogy is intriguing, it may be difficult for readers without a background in crystallization kinetics to grasp fully. More simplified explanations or visual aids could make this clearer.

2:Although the method improves scalability over GAN-based approaches, the process of clique enumeration can still be computationally expensive, potentially limiting applicability to extremely large graphs.

3:The experiments, though diverse, may benefit from including more real-world networks with varying characteristics to generalize the method's effectiveness further.

4:The paper does not thoroughly discuss the practical implementation details, such as computational resources required for different dataset sizes, which may affect the adoption of the model in real-world applications.
Yes
No ethics review needed.
1.Applicability of Physical Analogy: The analogy between community detection and crystallization lacks theoretical validation. Please clarify how crystallization principles (e.g., "stability," "cohesion") apply and impact core consistency and scalability.

2.Theoretical Support for CLANN: The model lacks theoretical analysis on convergence and complexity. Please provide complexity proof or detailed time complexity analysis, particularly for large datasets.

3.Parameter Sensitivity Analysis: The paper lacks a systematic sensitivity analysis of key hyperparameters (e.g., annealing temperature, loss weights). Please describe how performance varies with these parameters.

4.Applicability to Non-Clique Structures: CLANN’s reliance on cliques as cores raises questions about its adaptability to non-clique or noisy networks. Please test on such datasets to verify its general applicability.
1.Innovative Approach: The paper creatively relates community detection to crystallization kinetics, introducing an annealing mechanism to enhance community core consistency and scalability with the novel CLANN model.

2.Scalability Improvement: The unsupervised Transitive Annealer significantly reduces computational complexity compared to GANs and reinforcement learning, improving performance on large-scale datasets.

3.Significant Performance Gains: Experiments show that CLANN outperforms existing methods in accuracy and efficiency across various real-world datasets, highlighting its broad applicability.
In community detection, semi-supervised methods are widely used to identify specific communities; however, existing methods often rely on high-cost models like Generative Adversarial Networks (GAN) or reinforcement learning, resulting in suboptimal initial community cores and limited scalability. There are two main issues with these methods: first, community core inconsistency, as growth-based methods often start from random nodes or k-hop ego networks, which do not accurately represent the structural characteristics of the community; and second, poor growth scalability, as using GAN or reinforcement learning to increase community core candidates is computationally intensive and fails to efficiently leverage existing information, limiting the number of core candidates. To address these issues, the authors propose a community detection model, CLANN, based on annealing and crystallization kinetics. This model comprises two key components: the Nucleus Proposer, which selects completely connected subgraphs (i.e., cliques) as community cores and integrates four crystallization principles to enhance core consistency; and the Transitive Annealer, which uses a learning-free annealing mechanism to merge adjacent cliques into final communities, thereby reducing computational cost and improving scalability. Experimental results show that CLANN outperforms existing community detection methods in terms of accuracy and efficiency across multiple real-world datasets, demonstrating its effectiveness and adaptability in diverse network analysis scenarios.
1.Questionable Validity of Physical Analogy: The analogy between community detection and crystallization kinetics is debatable, particularly regarding the relevance of energy minimization in community detection.

2.Insufficient Explanation of Crystallization Dependency: The paper does not adequately clarify how the introduced crystallization principles apply to community detection and their role in model optimization.

3.Limited Adaptability to Different Network Structures: CLANN’s effectiveness on non-clique structures or noisy communities is not thoroughly assessed, raising doubts about its generalizability.

4.Lack of Theoretical Analysis: The method lacks in-depth theoretical validation, such as proofs of convergence and complexity for the Transitive Annealer, along with insufficient sensitivity analyses for hyperparameters.
Yes
No ethics review needed.
Q1: Eq. 9 is not the same as the loss in Algo. 1? I think this needs more explanation. Also, at the same time the $ \gamma _G$ should be performing a parameter sensitivity analysis.
S1: The approach of using crystallization principles, especially the spontaneous annealing process, provides a compelling and physics-informed basis for community formation.
S2: The learning-free Transitive Annealer circumvents computational issues tied to reinforcement learning, showing superior scalability over existing methods.
S3: Empirical results indicate that CLANN consistently achieves higher performance across diverse datasets, particularly excelling in detecting distinct community structures within hybrid graphs.
The paper proposes a novel approach for semi-supervised community detection, named CLique ANNealing (CLANN), which integrates principles from crystallization kinetics with community detection. To address the issues with scalability and core selection inconsistency, CLANN introduces two main components Nucleus Proposer and Transitive Annealer, draws an analogy between community detection and crystallization, treating communities as grains growing from smaller, well-defined "clique" cores. Experiments conducted on datasets show that CLANN outperforms state-of-the-art methods in both efficacy and efficiency.
W1: The CLANN model assumes high network density and consistency by analogizing community detection to crystallization growth. In practice, however, many graph networks (e.g., social networks, literature citation networks, etc.) may contain highly heterogeneous connectivity structures and low degree of node density, which could lead to difficulties for the annealing process to efficiently identify core or stable structures. In sparse or non-uniform networks, the use of an annealing process may lead to difficulties in forming coherent communities in the model, affecting the detection accuracy.
W2: CLANN pre-screens the candidate set by a filtering mechanism when initializing candidate community cores to improve efficiency. However, on large-scale datasets, this mechanism still requires a lot of computational resources to traverse and filter the candidate cores. Especially in real-time application scenarios, the initialization process may be too slow, affecting the application efficiency of the algorithm.
W3: For hyperparameters such as Transitive Threshold $\theta$, Temperature Prob. $P_{temp}$, further analysis is required.
Yes
No ethics review needed.
Given that some losses do not statistically significantly improve performance (e.g., Integrity-Based Loss), would it be possible to reduce certain losses to make the model simpler and emphasize the more effective components?
S1: The design of the method is inspired by the kinetics of the crystallization process, which provides strong motivation.

S2: Detailed formulas are used to express the approach more rigorously and scientifically.

S3: Extensive experiments are conducted to validate the method.
This manuscript introduces CLANN, a new community detection method motivated by the kinetics crystallization process. It integrates the Nucleus Proposer to learn to detect community cores and Transitive Annealer for spontaneous growth. Extensive experiments with various settings highlight the excellence of CLANN.
W1: I have two key concerns about the design of the loss functions

W1.1: In equation 7, the formulation of Integrity-Based Loss is used to guide the prediction of the state of undergrown, equilibrium, and overgrown states. It is modeled as a classification problem. However, the loss function used seems not to align with the Binary Cross Entropy. 

The binary cross-entropy loss is:

$
\text{BCE}(y, \hat{y}) = - \frac{1}{N} \sum_{i=1}^N \left( y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i) \right)
$

where:
- \( y_i \) is the true label (0 or 1),
- \( \hat{y}_i \) is the predicted probability of the positive class,
- \( N \) is the total number of samples. 

while the authors seem to mix up the order in the bracket:

$
\sum_{g=1}^3 \left( (y_g^k - 1) \log(\hat{y}_g^k) - y_g^k \log(1 - \hat{y}_g^k) \right)
$

W1.2: In the design of the integrity-based method, the overflow states are predefined, which limits the inclusion of many promising patterns, such as:
$(a_2 + \check{a}_2)$

===============

W2: I have two key concerns about the training process.

W2.1: A two-stage training method is used here: first, the Energy-Based Loss, Consistency-Based Loss, and Interface-Based Loss are trained together, followed by training the Integrity-Based Loss separately. This approach to multi-loss training is relatively unusual and lacks clear motivation. Won't the second loss disrupt the model trained in the first stage? How are these losses aligned?

W2.2: As seen in Figure 5, when Loss Amplification approaches 0, performance on many datasets actually improves, indicating that this loss does not enhance training and may even reduce performance (e.g., Consistency-Based Loss and Interface-Based Loss). Strangely, in the ablation study in Table 4, these losses appear to be beneficial.

===============

W3: Some claims and typos can be improved.

W3.1: The authors argue that previous reinforcement-learning-based methods face scalability issues (Line 017), and that the proposed method has better scalability compared to these approaches. CLARE itself is also a reinforcement learning-based method (Line 984). As shown in Figure 4, NP and CLANN demonstrate poorer scalability compared to CLARE. In the DBLP and LiveJournal datasets, NP and CLANN even exhibit faster growth in runtime.

W3.2: Line 157, "Propose" => "Proposer"
semi-supervised community detection approach premised on crystallization kinetics
@inproceedings{
anonymous2024new,
title={New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=jQ5T1Pbnx7},
note={under review}
}
Semi-supervised community detection methods are widely used for identifying specific communities due to the label scarcity. Existing semi-supervised community detection methods typically involve two learning stages \ie, learning in both initial identification and subsequent adjustment, which often starts from an unreasonable community core candidate.
Moreover, these methods encounter scalability issues because they depend on reinforcement learning and generative adversarial networks, leading to higher computational costs and restricting the selection of candidates. 
To address these limitations, we draw a parallel between crystallization kinetics and community detection to integrate the spontaneity of the annealing process into community detection.
Specifically, we liken community detection to identifying a crystal subgrain (core) that expands into a complete grain (community) through a process similar to annealing. Based on this finding, we propose CLique ANNealing (CLANN), which applies kinetics concepts to community detection by integrating these principles into the optimization process to strengthen the consistency of the community core. Subsequently, a learning-free Transitive Annealer was employed to refine the first-stage candidates by merging neighboring cliques and repositioning the community core, enabling a spontaneous growth process that enhances scalability.
Extensive experiments on diverse community detection datasets demonstrate that CLANN outperforms state-of-the-art methods across multiple real-world datasets, showcasing its exceptional efficacy and efficiency in community detection.
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Semi-supervised Community Detection Clique Annealing Crystallization Kinetics
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New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics
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