Centrality Metric for Dynamic Networks

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.Comment: in KDD workshop on Mining and Learning in Graphs (MLG

Analyzing microblogs with affinity propagation

Recently, there has been a great deal of interest in analyz-ing inherent structures in posts on microblogs such as Twit-ter. While many works utilize a well-known topic modeling technique, we instead propose to apply Affinity Propaga-tion [4] (AP) to analyze such a corpus, and we hypothesize that AP may provide different perspective to the traditional approach. Our preliminary analysis raises some interesting facts and issues, which suggest future research directions

Limited Attention and Centrality in Social Networks

How does one find important or influential people in an online social network? Researchers have proposed a variety of centrality measures to identify individuals that are, for example, often visited by a random walk, infected in an epidemic, or receive many messages from friends. Recent research suggests that a social media users' capacity to respond to an incoming message is constrained by their finite attention, which they divide over all incoming information, i.e., information sent by users they follow. We propose a new measure of centrality --- limited-attention version of Bonacich's Alpha-centrality --- that models the effect of limited attention on epidemic diffusion. The new measure describes a process in which nodes broadcast messages to their out-neighbors, but the neighbors' ability to receive the message depends on the number of in-neighbors they have. We evaluate the proposed measure on real-world online social networks and show that it can better reproduce an empirical influence ranking of users than other popular centrality measures.Comment: in Proceedings of International Conference on Social Intelligence and Technology (SOCIETY2013

Cancer-related Fatigue in Patients with Advanced Cancer Treated with Autonomic Nerve Pharmacopuncture

AbstractThe purpose of this study was to observe the effects of autonomic nerve pharmacopuncture (ANP) treatment on cancer-related fatigue (CRF) in patients with advanced cancer. This observational case study was conducted at the East West Cancer Center of Daejeon University's Dunsan Korean Medical Hospital. Two patients were observed. One patient was diagnosed with left thymic cancer metastatic to the left pleura. The other patient had terminal-stage cervical cancer with iliac bone and lumbar 5 metastases. We injected mountain ginseng pharmacopuncture (MGP) into acupoints alongside the spine (Hua-Tuo-Jia-Ji-Xue, EX B2). We examined the patients for CRF using the Korean version of the Revised Piper Fatigue Scale (RPFS-K), which is a self-assessment tool. The scores on the RPFS-K for both patients tended to decrease during the treatment. Laboratory findings, including hematological changes, were also checked. Liver and renal function tests showed that the treatment was safe. Although further large-population studies are necessary, this case study suggests that ANP has a favorable effect on CRF in patients with advanced cancer