arXiv subjects

Zhen Tan

Publications and source records attributed to Zhen Tan.

115 records · Page 7Linked to original sources

arXiv2022

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an E-commerce platform with scarce records or diagnoses for rare diseases on a patient similarity graph. To tackle such challenging label scarcity issues in the non-Euclidean graph domain, meta-learning has become a successful and predominant paradigm. More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed. In this work, we empirically demonstrate the potential of an alternative framework, \textit{Transductive Linear Probing}, that transfers pretrained node embeddings, which are learned from graph contrastive learning methods. We further extend the setting of few-shot node classification from standard fully supervised to a more realistic self-supervised setting, where meta-learning methods cannot be easily deployed due to the shortage of supervision from training classes. Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. We hope this work can shed new light on few-shot node classification problems and foster future research on learning from scarcely labeled instances on graphs.

cs.LG↗

arXiv2022

Supervised Graph Contrastive Learning for Few-shot Node Classification

Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called \textit{few-shot node classification}. A predominant approach to this problem resorts to \textit{episodic meta-learning}. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.

cs.LG↗

arXiv2021

Graph Few-shot Class-incremental Learning

The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical problem, Graph Few-shot Class-incremental (Graph FCL) problem, where the graph model is tasked to classify both newly encountered classes and previously learned classes. Towards that purpose, we put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes, so as to produce an arbitrary number of training episodes for our model to practice the incremental learning skill. Furthermore, we design a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes. To employ the topological knowledge, we add a node-level attention module to adjust the prototype representation. Our model not only achieves greater stability of old knowledge consolidation, but also acquires advantageous adaptability to new knowledge with very limited data samples. Extensive experiments on three real-world datasets, including Amazon-clothing, Reddit, and DBLP, show that our framework demonstrates remarkable advantages in comparison with the baseline and other related state-of-the-art methods.

cs.LG↗

arXiv2020

Degree-Aware Alignment for Entities in Tail

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA. To mitigate the issue, we revisit and investigate into the conventional EA pipeline in pursuit of elegant performance. For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings. For alignment, under a novel complementary framework of consolidating structural and name signals, we identify entity's degree as important guidance to effectively fuse two different sources of information. To this end, a degree-aware co-attention network is conceived, which dynamically adjusts the significance of features in a degree-aware manner. For post-alignment, we propose to complement original KGs with facts from their counterparts by using confident EA results as anchors via iterative training. Comprehensive experimental evaluations validate the superiority of our proposed techniques.

cs.AI↗

arXiv2014

Temperature dependent characteristics of GaSb p-channel MOSFETs with Si-implanted source and drain

GaSb p-channel MOSFETs with an atomic layer deposited Al2O3 gate dielectric and a self-aligned Si implanted source/drain are demonstrated. Thermal anneal conditions are optimized for the source/drain impurity activation. Temperature dependent electrical characteristics are investigated. Different electrical behaviors are observed in two different temperature regions and the mechanisms underneath are proposed. Off-state drain current is generation current dominated in the low temperature regions and is diffusion current dominated in the high temperature regions.

cond-mat.mtrl-sci↗

arXiv2013

Effects of ozone post deposition treatment on interfacial and electrical characteristics of atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates

Atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates were treated by in-situ ozone post deposition treatment (PDT). The effects of ozone PDT on the interfacial and electrical properties of Al2O3 and HfO2 gate dielectric films on GaSb substrates were investigated carefully. It is found that the dielectric quality and the interfacial properties of the Al2O3 and HfO2 films are improved by ozone PDT. After in-situ ozone PDT for 5 min, the Al2O3 and HfO2 films on GaSb substrates exhibit improved electrical and interfacial properties, such as reduced frequency dispersion, gate leakage current, border traps and interface traps. Interface trap density is reduced by ~24% for the Al2O3/GaSb stacks and ~27% for the HfO2/GaSb stacks. In-situ ozone PDT is proved to be a promising technique in improving the quality of high-k gate stacks on GaSb substrates.

cond-mat.mtrl-sci↗

arXiv2013

Improved Interfacial and Electrical Properties of GaSb Metal Oxide Semiconductor Devices Passivated with Acidic (NH4)2S Solution

Surface passivation with acidic (NH4)2S solution is shown to be effective in improving the interfacial and electrical properties of HfO2/GaSb metal oxide semiconductor devices. Compared with control samples, those treated with acidic (NH4)2S solution showed great improvements in frequency dispersion, gate leakage current and interface trap density. These improvements were attributed to the acidic (NH4)2S solution enhancing passivation of the substrates, which was analyzed from the perspective of chemical mechanism and confirmed by X-ray photoelectron spectroscopy and high-resolution cross-sectional transmission electron microscopy.

cond-mat.mtrl-sci↗