There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Networked: the new social operating system

The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.

User Identity Linkage across Online Social Networks: A Review

The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.

Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.

Structure Based User Identification across Social Networks

Online BIM visualization system is the first and primary task for building web applications of BIM. Light-weighted, cross-platform and open are three basic rules when building online BIM visualization systems. Currently, some efforts have been made on BIM visualization. However, most of them are designed for local BIM, and the online ones neglect the network load (without light-weighted) or are platform dependent (without cross-platform). This study develops a novel online BIM visualization system based on IFC and WebGL, termed as WebBIM. WebBIM firstly converts the raw IFC geometry data into triangles, light-weights the BIM geometry data by sharing the geometry data among the object instances generated from the same facility component, compresses the geometry, and directly renders the decompressed triangular BIM data in web browsers. Finally, empirical studies from extensive real projects’ BIM data show that WebBIM is efficient, capable of visualization of large BIM files and compatible with mainstream devices.

Cross-platform online visualization system for open BIM based on WebGL

Pathfinding is a fundamental problem for many areas, e.g., robotics, automation, computer-aided design, and computer graphics. Although outdoor pathfinding is fledged, indoor pathfinding remains a challenge due to the lack of indoor maps. Currently, some efforts have utilized building information modeling (BIM) to generate either the grid-based map or the topological map. However, either the grid-based map or the topological map is not sufficient to provide accurate and efficient pathfinding service. This article proposes a novel grid-topological map and develops an accurate and efficient indoor pathfinding scheme based on BIM. The grid-topological map is modeled jointly adopting the advantages of both the grid-based map and the topological map. First, the grid-based map is generated using the BIM data by extracting and mapping geometric and semantic data into planar grids. Second, a grid thinning algorithm is proposed to produce the topological map directly using the grid-based map. Third, a grid-topological map is presented by combining both the grid-based map and topological map. On top of the grid-topological map, an accurate and efficient pathfinding algorithm is developed. Empirical studies proved the effectiveness of the grid-topological map, as well as the accuracy and efficiency of the proposed indoor pathfinding algorithm.

Accurate and Efficient Indoor Pathfinding Based on Building Information Modeling Data

Building Information Modeling (BIM) is a promising technology for building informatics. Currently, an increasing number of applications adopt BIM to improve the building operations and facility management. In these applications, matching real-world facilities to the corresponding BIM items is a fundamental yet challenging task. This study addresses this issue using Natural Language Processing. Firstly, a novel BIM hierarchy tree (HiTree) is proposed to model the original spatial structure relationships of a BIM. Then, the locations of facilities are extracted from natural language through processes of word segmentation, keyword extraction, and semantic disambiguation. Thirdly, an algorithm that matches real-world facilities to the BIM data is developed using the HiTree and the extracted locations. Finally, a concrete case for a 35,000 m
 2
 library is presented to verify the effectiveness of the proposed solution. BIM has become a common paradigm in the construction industry, and our scheme can facilitate more applications of BIM in building operations and facility management. One of the most representative applications is integrating the BIM data and information within IoT (Internet of Things) system intelligently by matching the BIM data to real-world facilities.

Xiaoping Zhou

Papers

Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

Structure Based User Identification across Social Networks

Cross-platform online visualization system for open BIM based on WebGL

Accurate and Efficient Indoor Pathfinding Based on Building Information Modeling Data

Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing