Frequent Subgraph Mining in Outerplanar Graphs

In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

Frequent Subgraph Mining in Outerplanar Graphs

In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we define the class of so called tenuous outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for tenuous outerplanar graphs that works in incremental polynomial time, and evaluate the algorithm empirically on the NCI molecular graph dataset

H\"older-type inequalities and their applications to concentration and correlation bounds

Let $Y_v, v\in V,$ be $[0,1]$-valued random variables having a dependency graph $G=(V,E)$. We show that $$\mathbb{E}\left[\prod_{v\in V} Y_{v} \right] \leq \prod_{v\in V} \left\{ \mathbb{E}\left[Y_v^{\frac{\chi_b}{b}}\right] \right\}^{\frac{b}{\chi_b}},$$ where $\chi_b$ is the $b$-fold chromatic number of $G$. This inequality may be seen as a dependency-graph analogue of a generalised H\"older inequality, due to Helmut Finner. Additionally, we provide applications of H\"older-type inequalities to concentration and correlation bounds for sums of weakly dependent random variables.Comment: 15 page

Learning from networked examples

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We show that the classic approach of ignoring this problem potentially can have a harmful effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in this networked setting, providing significantly improved results. An important component of our approach is formed by efficient sample weighting schemes, which leads to novel concentration inequalities

Building Alliances: Collaboration between CAUSA and the Rural Organizing Project in Oregon

This ethnography examines the components that allow quality solidarity work to happen between organizations with leadership and constituencies that are primarily people of color and primarily white, respectively. CAUSA (an immigrant rights coalition) and the Rural Organizing Project (ROP) of Oregon have developed a working relationship over ten years that has contributed to numerous victories for immigrant and farm worker rights, as well as greater consciousness among white rural activists of what it means to provide support as anti-racist allies. Because Oregon has a relatively small population (three million), and progressive organizations tend to know each other, the relationship provides an opportunity to study how such organizations manage power and historical inequalities in a manner suited for success. Ethnographer Lynn Stephen has conducted in-depth interviews with organizational leaders and members as a way to explore the history and lessons learned from the collaborative work between the two organizations. Key findings include the importance of both in-depth and sustained dialogue around the key values of work, and staff training around the issues involved with connecting to the other organization. The organizations use these techniques to build common ground. Hence, collaborative capacity can be mobilized quickly to support each other's actions as needed