A persistently popular topic in online social networks is the rapid and
accurate discovery of bot accounts to prevent their invasion and harassment of
genuine users. We propose a unified embedding framework called BOTTRINET, which
utilizes textual content posted by accounts for bot detection based on the
assumption that contexts naturally reveal account personalities and habits.
Content is abundant and valuable if the system efficiently extracts bot-related
information using embedding techniques. Beyond the general embedding framework
that generates word, sentence, and account embeddings, we design a triplet
network to tune the raw embeddings (produced by traditional natural language
processing techniques) for better classification performance. We evaluate
detection accuracy and f1score on a real-world dataset CRESCI2017, comprising
three bot account categories and five bot sample sets. Our system achieves the
highest average accuracy of 98.34% and f1score of 97.99% on two
content-intensive bot sets, outperforming previous work and becoming
state-of-the-art. It also makes a breakthrough on four content-less bot sets,
with an average accuracy improvement of 11.52% and an average f1score increase
of 16.70%.

BOTTRINET 使用嵌入技术，提出一个统一的嵌入框架进行机器人检测，并设计了三重神经网络进一步提高分类性能，成功实现对真实世界数据集的最高平均准确度和 f1 分数，达到 98.34％和 97.99％，且对于四个内容无关的 bot 集合，平均准确度提高了 11.52％，f1 分数提高了 16.70％。

BotTriNet: 通过度量学习实现社交机器人检测的统一高效嵌入

BotTriNet: A Unified and Efficient Embedding for Social Bots Detection  via Metric Learning

Knowledge graphs suffer from sparsity which degrades the quality of
representations generated by various methods. While there is an abundance of
textual information throughout the web and many existing knowledge bases,
aligning information across these diverse data sources remains a challenge in
the literature. Previous work has partially addressed this issue by enriching
knowledge graph entities based on "hard" co-occurrence of words present in the
entities of the knowledge graphs and external text, while we achieve "soft"
augmentation by proposing a knowledge graph enrichment and embedding framework
named Edge. Given an original knowledge graph, we first generate a rich but
noisy augmented graph using external texts in semantic and structural level. To
distill the relevant knowledge and suppress the introduced noise, we design a
graph alignment term in a shared embedding space between the original graph and
augmented graph. To enhance the embedding learning on the augmented graph, we
further regularize the locality relationship of target entity based on negative
sampling. Experimental results on four benchmark datasets demonstrate the
robustness and effectiveness of Edge in link prediction and node
classification.

该论文提出了一种名为 Edge 的知识图谱增强和嵌入框架，通过外部文本语义和结构层面的知识图谱增强，设计图对齐术语，负采样和嵌入学习，Edge 在链接预测和节点分类方面表现出鲁棒性和高效性。

边缘：用外部文本丰富知识图谱嵌入

Edge: Enriching Knowledge Graph Embeddings with External Text

Node representation learning for directed graphs is critically important to
facilitate many graph mining tasks. To capture the directed edges between
nodes, existing methods mostly learn two embedding vectors for each node,
source vector and target vector. However, these methods learn the source and
target vectors separately. For the node with very low indegree or outdegree,
the corresponding target vector or source vector cannot be effectively learned.
In this paper, we propose a novel Directed Graph embedding framework based on
Generative Adversarial Network, called DGGAN. The main idea is to use
adversarial mechanisms to deploy a discriminator and two generators that
jointly learn each node's source and target vectors. For a given node, the two
generators are trained to generate its fake target and source neighbor nodes
from the same underlying distribution, and the discriminator aims to
distinguish whether a neighbor node is real or fake. The two generators are
formulated into a unified framework and could mutually reinforce each other to
learn more robust source and target vectors. Extensive experiments show that
DGGAN consistently and significantly outperforms existing state-of-the-art
methods across multiple graph mining tasks on directed graphs.

本文提出基于生成对抗网络的新型有向图嵌入框架 DGGAN，该框架可以联合学习每个节点的源向量和目标向量，并且在多项有向图挖掘任务上优于现有的最先进方法。