Mechanisms of Controlled Sharing for Social Networking Users.

Social networking sites are attracting hundreds of millions of users to share information online. One critical task for all of these users is to decided the right audience with which to share. The decision about the audience can be at a coarse level (e.g., deciding to share with everyone, friends of friends, or friends), or at a fine level (e.g., deciding to share with only some of the friends). Performing such controlled sharing tasks can be tedious and error-prone to most users. An active social networking user can have hundreds of contacts. Therefore, it can be difficult to pick the right subset of them to share with. Also, a user can create a lot of content, and each piece of it can be shared to a different audience. In this dissertation, I perform an extensive study of the controlled sharing problem and propose and implement a series of novel tools that help social networking users better perform controlled sharing. I propose algorithms that automatically generate a recommended audience for both static profile items as well as real-time generated content. To help users better understand the recommendations, I propose a relationship explanation tool that helps users understand the relationship between a pair of friends. I perform extensive evaluations to demonstrate the efficiency and effectiveness of our tools. With our tools, social networking users can control sharing more accurately with less effort. Finally, I also study an existing controlled-sharing tool, namely the circle sharing tool for Google+. I perform extensive data analyses and examine the impact of friend groups sharing behaviors on the development of the social network.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97999/1/ljfang_1.pd

Exact analytical solution of average path length for Apollonian networks

The exact formula for the average path length of Apollonian networks is found. With the help of recursion relations derived from the self-similar structure, we obtain the exact solution of average path length, $\bar{d}_t$, for Apollonian networks. In contrast to the well-known numerical result $\bar{d}_t \propto (\ln N_t)^{3/4}$ [Phys. Rev. Lett. \textbf{94}, 018702 (2005)], our rigorous solution shows that the average path length grows logarithmically as $\bar{d}_t \propto \ln N_t$ in the infinite limit of network size $N_t$. The extensive numerical calculations completely agree with our closed-form solution.Comment: 8 pages, 4 figure

Maximal planar scale-free Sierpinski networks with small-world effect and power-law strength-degree correlation

Many real networks share three generic properties: they are scale-free, display a small-world effect, and show a power-law strength-degree correlation. In this paper, we propose a type of deterministically growing networks called Sierpinski networks, which are induced by the famous Sierpinski fractals and constructed in a simple iterative way. We derive analytical expressions for degree distribution, strength distribution, clustering coefficient, and strength-degree correlation, which agree well with the characterizations of various real-life networks. Moreover, we show that the introduced Sierpinski networks are maximal planar graphs.Comment: 6 pages, 5 figures, accepted by EP

Synergistic effect of CD47 blockade in combination with cordycepin treatment against cancer

Cordycepin is widely considered a direct tumor-suppressive agent. However, few studies have investigated as the effect of cordycepin therapy on the tumor microenvironment (TME). In our present study, we demonstrated that cordycepin could weaken the function of M1-like macrophages in the TME and also contribute to macrophage polarization toward the M2 phenotype. Herein, we established a combined therapeutic strategy combining cordycepin and an anti-CD47 antibody. By using single-cell RNA sequencing (scRNA-seq), we showed that the combination treatment could significantly enhance the effect of cordycepin, which would reactivate macrophages and reverse macrophage polarization. In addition, the combination treatment could regulate the proportion of CD8+ T cells to prolong the progression-free survival (PFS) of patients with digestive tract malignancies. Finally, flow cytometry validated the changes in the proportions of tumor-associated macrophages (TAMs) and tumor-infiltrating lymphocytes (TILs). Collectively, our findings suggested that the combination treatment of cordycepin and the anti-CD47 antibody could significantly enhance tumor suppression, increase the proportion of M1 macrophages, and decrease the proportion of M2 macrophages. In addition, the PFS in patients with digestive tract malignancies would be prolonged by regulating CD8+ T cells

Enhancing text clustering by leveraging Wikipedia semantics

Most traditional text clustering methods are based on “bag of words ” (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved