21,171 research outputs found
Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search
This paper proposes the use of graph pattern matching for investigative graph
search, which is the process of searching for and prioritizing persons of
interest who may exhibit part or all of a pattern of suspicious behaviors or
connections. While there are a variety of applications, our principal
motivation is to aid law enforcement in the detection of homegrown violent
extremists. We introduce investigative simulation, which consists of several
necessary extensions to the existing dual simulation graph pattern matching
scheme in order to make it appropriate for intelligence analysts and law
enforcement officials. Specifically, we impose a categorical label structure on
nodes consistent with the nature of indicators in investigations, as well as
prune or complete search results to ensure sensibility and usefulness of
partial matches to analysts. Lastly, we introduce a natural top-k ranking
scheme that can help analysts prioritize investigative efforts. We demonstrate
performance of investigative simulation on a real-world large dataset.Comment: 8 pages, 6 figures. Paper to appear in the Fosint-SI 2016 conference
proceedings in conjunction with the 2016 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining ASONAM 201
Exploring Communities in Large Profiled Graphs
Given a graph and a vertex , the community search (CS) problem
aims to efficiently find a subgraph of whose vertices are closely related
to . Communities are prevalent in social and biological networks, and can be
used in product advertisement and social event recommendation. In this paper,
we study profiled community search (PCS), where CS is performed on a profiled
graph. This is a graph in which each vertex has labels arranged in a
hierarchical manner. Extensive experiments show that PCS can identify
communities with themes that are common to their vertices, and is more
effective than existing CS approaches. As a naive solution for PCS is highly
expensive, we have also developed a tree index, which facilitate efficient and
online solutions for PCS
Leveraging Language Representation for Material Recommendation, Ranking, and Exploration
Data-driven approaches for material discovery and design have been
accelerated by emerging efforts in machine learning. While there is enormous
progress towards learning the structure to property relationship of materials,
methods that allow for general representations of crystals to effectively
explore the vast material search space and identify high-performance candidates
remain limited. In this work, we introduce a material discovery framework that
uses natural language embeddings derived from material science-specific
language models as representations of compositional and structural features.
The discovery framework consists of a joint scheme that, given a query
material, first recalls candidates based on representational similarity, and
ranks the candidates based on target properties through multi-task learning.
The contextual knowledge encoded in language representations is found to convey
information about material properties and structures, enabling both similarity
analysis for recall, and multi-task learning to share information for related
properties. By applying the discovery framework to thermoelectric materials, we
demonstrate diversified recommendations of prototype structures and identify
under-studied high-performance material spaces, including halide perovskite,
delafossite-like, and spinel-like structures. By leveraging material language
representations, our framework provides a generalized means for effective
material recommendation, which is task-agnostic and can be applied to various
material systems
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
In this paper, we present our work towards comparing on-line and off-line
evaluation metrics in the context of small e-commerce recommender systems.
Recommending on small e-commerce enterprises is rather challenging due to the
lower volume of interactions and low user loyalty, rarely extending beyond a
single session. On the other hand, we usually have to deal with lower volumes
of objects, which are easier to discover by users through various
browsing/searching GUIs.
The main goal of this paper is to determine applicability of off-line
evaluation metrics in learning true usability of recommender systems (evaluated
on-line in A/B testing). In total 800 variants of recommending algorithms were
evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based,
novelty and diversity evaluation. The off-line results were afterwards compared
with on-line evaluation of 12 selected recommender variants and based on the
results, we tried to learn and utilize an off-line to on-line results
prediction model.
Off-line results shown a great variance in performance w.r.t. different
metrics with the Pareto front covering 68\% of the approaches. Furthermore, we
observed that on-line results are considerably affected by the novelty of
users. On-line metrics correlates positively with ranking-based metrics (AUC,
MRR, nDCG) for novice users, while too high values of diversity and novelty had
a negative impact on the on-line results for them. For users with more visited
items, however, the diversity became more important, while ranking-based
metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc
Knowledge networks in the Dutch aviation industry: the proximity paradox
The importance of geographical proximity for interaction and knowledge sharing has been discussed extensively in economic geography in recent years. There is increasing consensus that it is just one out of many types of proximities that might be relevant. We argue that proximity may be a crucial driver for agents to connect and exchange knowledge, but too much proximity between these agents on any of the dimensions might harm their innovative performance at the same time. In a study on knowledge networks in the Dutch aviation industry, we test this so-called proximity paradox empirically. We find evidence that the proximity paradox holds to some degree. Our study clearly shows that cognitive, social and geographical proximity are crucial for explaining the knowledge network of the Dutch aviation industry. But while it takes cognitive, social and geographical proximity to exchange knowledge, we found evidence that proximity lowers firms's innovative performance, but only in the cognitive dimension.proximity, paradox, social network analysis, knowledge networks, aviation
Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns
Many investigations of scientific collaboration are based on statistical
analyses of large networks constructed from bibliographic repositories. These
investigations often rely on a wealth of bibliographic data, but very little or
no other information about the individuals in the network, and thus, fail to
illustrate the broader social and academic landscape in which collaboration
takes place. In this article, we perform an in-depth longitudinal analysis of a
relatively small network of scientific collaboration (N = 291) constructed from
the bibliographic record of a research center involved in the development and
application of sensor network and wireless technologies. We perform a
preliminary analysis of selected structural properties of the network,
computing its range, configuration and topology. We then support our
preliminary statistical analysis with an in-depth temporal investigation of the
assortative mixing of selected node characteristics, unveiling the researchers'
propensity to collaborate preferentially with others with a similar academic
profile. Our qualitative analysis of mixing patterns offers clues as to the
nature of the scientific community being modeled in relation to its
organizational, disciplinary, institutional, and international arrangements of
collaboration.Comment: Scientometrics (In press
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