Classifying scientific publications according to Field-of-Science (FoS)
taxonomies is of crucial importance, allowing funders, publishers, scholars,
companies and other stakeholders to organize scientific literature more
effectively. Most existing works address classification either at venue level
or solely based on the textual content of a research publication. We present
SciNoBo, a novel classification system of publications to predefined FoS
taxonomies, leveraging the structural properties of a publication and its
citations and references organised in a multilayer network. In contrast to
other works, our system supports assignments of publications to multiple fields
by considering their multidisciplinarity potential. By unifying publications
and venues under a common multilayer network structure made up of citing and
publishing relationships, classifications at the venue-level can be augmented
with publication-level classifications. We evaluate SciNoBo on a publications'
dataset extracted from Microsoft Academic Graph and we perform a comparative
analysis against a state-of-the-art neural-network baseline. The results reveal
that our proposed system is capable of producing high-quality classifications
of publications.

本研究提出了一种新的科学文献分类系统 SciNoBo，它采用多层网络结构，基于文献结构和引文引用，将文献分类到预定义的 Field-of-Science（FoS）分类法中，并能够支持多个领域的多学科学术文献分类，并在来自 Microsoft Academic Graph 的数据集上表现出高质量的分类能力。

SciNoBo：一种基于层级多标签分类的科学出版物分类器

SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications

We present the Influence Flower, a new visual metaphor for the influence
profile of academic entities, including people, projects, institutions,
conferences, and journals. While many tools quantify influence, we aim to
expose the flow of influence between entities. The Influence Flower is an
ego-centric graph, with a query entity placed in the centre. The petals are
styled to reflect the strength of influence to and from other entities of the
same or different type. For example, one can break down the incoming and
outgoing influences of a research lab by research topics. The Influence Flower
uses a recent snapshot of Microsoft Academic Graph, consisting of 212million
authors, their 176 million publications, and 1.2 billion citations. An
interactive web app, Influence Map, is constructed around this central metaphor
for searching and curating visualisations. We also propose a visual comparison
method that highlights change in influence patterns over time. We demonstrate
through several case studies that the Influence Flower supports data-driven
inquiries about the following: researchers' careers over time; paper(s) and
projects, including those with delayed recognition; the interdisciplinary
profile of a research institution; and the shifting topical trends in
conferences. We also use this tool on influence data beyond academic citations,
by contrasting the academic and Twitter activities of a researcher.

本文介绍了影响力之花，这是一种新的视觉隐喻，用于表示学术实体的影响力档案，包括个人、项目、学术机构、会议和期刊，该方法利用 Microsoft 学术图表构建，采用中心主体浏览方式，支持基于数据的学术调查及可视化比较方法。