Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of “across-network Weisfeiler-Lehman relabeling” and “proximity-preserving representation learning”. The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors’ connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the “exact matching” scenario.

Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that “overly-close” user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.

The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and `curse of dimensionality' and other restrictions made them insufficiently effective in complex data, e.g., different attribute weights, high-dimensionality, containing feature noise, nonlinearity, etc. This is also the main reason that the existing noise filtering methods were not widely applied and formed an effective learning framework. To address this problem, we propose here a complete and efficient random forest method (CRF) specifically for the class noise detection by simulating the grid generation and expansion. The CRF is not based on distance measures or overall distribution or classifiers; besides, the voting mechanism makes it able to effectively process datasets containing feature noise. Furthermore, we introduce CRF based class noise filtering learning framework (CRF-NFL) and derive its mathematical model. The framework is then applied to many widely used classifiers including some state-of-the-art algorithms, e.g., k-means tree, GBDT, and XGBoost. Moreover, its parallelized is designed for large-scale data. The CRF-NFL show much better generalizability than the conventional classifiers and the relative density-based method, which is the most effective noise filtering method as far as we know. All research has formed an open source library, called CRF-NFL: http://www.cquptshuyinxia.com/CRF-NFL.html.

Class noise can cause overfitting of learning methods and deteriorate their generalizability. Although relative density can detect class noise effectively, it has a time complexity of O(N2) and a low efficiency. To address this problem, by introducing the granular computing into the relative density model, this paper proposes a Multi-Granularity Relative Density (MGRD) model for class noise detection. In an experiment, we tested the effect of parameters on its performance under classifiers. The experimental results on benchmark data sets demonstrated that it had a higher efficiency than conventional methods. In addition, it exhibited better generalizability in comparison with the conventional methods because of the good robustness of granular computing on many cases.

In the human cognition learning, the noise self-filtering filters the noise data by itself, making the human brain very robust. Although k-Nearest Neighbors (kNN) can decrease the affection of noise data by optimizing the parameter k, noise data still may deteriorate the learning results to an extent on various data. Therefore, this paper proposes the strategy for kNNs. Here, we formalize such training strategies in the context of kNNs, and they are called with “Noise Self-filtering k-Nearest Neighbors” (NSF-kNNs). As its name suggests, it is determined by the model itself based on what it has already learned, as oppose to some predefined heuristic criteria. We derive the mathematical model of the NSF-kNNs, which is an algorithm framework. The NSF-kNNs are compared with the exact kNNs and as far as we know the most efficient approximate kNN [1]. The results show the achievement of improvements in the robustness and generalizability on various data sets.

Clustering is an important technique in data mining. In this paper, we introduce a new clustering algorithm. This algorithm, based on granular computing, constructs a hypergraph (simplicial complex) by the hypergraph bisection algorithm. It will discover the similarities and associations among documents. In some experiments on Web data, the proposed algorithm is used; the results are quite satisfactory. © 2009 Wiley Periodicals, Inc.