Link prediction problem has increasingly become prominent in many domains
such as social network analyses, bioinformatics experiments, transportation
networks, criminal investigations and so forth. A variety of techniques has
been developed for link prediction problem, categorized into 1) similarity
based approaches which study a set of features to extract similar nodes; 2)
learning based approaches which extract patterns from the input data; 3)
probabilistic statistical approaches which optimize a set of parameters to
establish a model which can best compute formation probability. However,
existing literatures lack approaches which utilize strength of each approach by
integrating them to achieve a much more productive one. To tackle the link
prediction problem, we propose an approach based on the combination of first
and second group methods; the existing studied works use just one of these
categories. Our two-phase developed method firstly determines new features
related to the position and dynamic behavior of nodes, which enforce the
approach more efficiency compared to approaches using mere measures. Then, a
subspace clustering algorithm is applied to group social objects based on the
computed similarity measures which differentiate the strength of clusters;
basically, the usage of local and global indices and the clustering information
plays an imperative role in our link prediction process. Some extensive
experiments held on real datasets including Facebook, Brightkite and HepTh
indicate good performances of our proposal method. Besides, we have
experimentally verified our approach with some previous techniques in the area
to prove the supremacy of ours.

提出一种结合第一和第二组方法的两阶段方法，其中第一阶段确定与节点位置和动态行为相关的新特征，第二阶段应用子空间聚类算法对社会对象进行分组，以区分集群的强度，在 Facebook、Brightkite 和 HepTh 等真实数据集上进行了广泛的实验，并与该领域的一些先前技术进行了实验验证，证明了该方法的优越性。

利用本地和全局特征提升社交网络的链接预测：一种基于聚类的方法

Improving Link Prediction in Social Networks Using Local and Global  Features: A Clustering-based Approach

Text classification of unseen classes is a challenging Natural Language
Processing task and is mainly attempted using two different types of
approaches. Similarity-based approaches attempt to classify instances based on
similarities between text document representations and class description
representations. Zero-shot text classification approaches aim to generalize
knowledge gained from a training task by assigning appropriate labels of
unknown classes to text documents. Although existing studies have already
investigated individual approaches to these categories, the experiments in
literature do not provide a consistent comparison. This paper addresses this
gap by conducting a systematic evaluation of different similarity-based and
zero-shot approaches for text classification of unseen classes. Different
state-of-the-art approaches are benchmarked on four text classification
datasets, including a new dataset from the medical domain. Additionally, novel
SimCSE and SBERT-based baselines are proposed, as other baselines used in
existing work yield weak classification results and are easily outperformed.
Finally, the novel similarity-based Lbl2TransformerVec approach is presented,
which outperforms previous state-of-the-art approaches in unsupervised text
classification. Our experiments show that similarity-based approaches
significantly outperform zero-shot approaches in most cases. Additionally,
using SimCSE or SBERT embeddings instead of simpler text representations
increases similarity-based classification results even further.

本文对不同的基于相似性和零样本方法进行了系统评估，对四个文本分类数据集进行了基准测试，包括来自医疗领域的新数据集。此外，提出了新的 SimCSE 和 SBERT 基线，其中使用 SimCSE 或 SBERT 嵌入而不是简单的文本表示进一步增强了基于相似性的分类结果。在无监督文本分类方面，提出了新的基于相似性的 Lbl2TransformerVec 方法，其性能优于先前的最先进方法。我们的实验表明，大多数情况下，基于相似性的方法显着优于零样本方法。