Search Results for author:
Found 16 papers, 2 papers with code
A performance characteristic curve for model evaluation: the application in information diffusion prediction
We propose a metric based on information entropy to quantify the randomness in diffusion data, then identify a scaling pattern between the randomness and the prediction accuracy of the model.
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites.
An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner
More importantly, our model is used to test the correctness of the explanations generated by the post-hoc method, the results show that the post-hoc method is not always reliable.
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives
Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method.
Scalable and Effective Conductance-based Graph Clustering
Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs.
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising
Specifically, based on the block-matching, the color image is decomposed into overlapping RGB patches.
The maximum capability of a topological feature in link prediction
The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links.
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients.
CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks
Cascade prediction aims at modeling information diffusion in the network.
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling
Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results.
Quantifying the noise in bursty gene expression under regulation by small RNAs
This analytical solution allows us to better analyze the noise and stochastic deviation of protein level.
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks
The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control.
The evolution of network controllability in growing networks
The study of network structural controllability focuses on the minimum number of driver nodes needed to control a whole network.
Physics and Society
From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information
The hyponym-hypernym relation is an essential element in the semantic network.
Measuring similarity in co-occurrence data using ego-networks
Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach.
