Update README.

README.md

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-FlashGraph is an SSD-based graph analysis framework that we designed to process
-graphs with billions of vertices and hundreds of billions of edges or even larger.
-We extend FlashGraph to support processing more data structures such as sparse
-matrices and dense matrices. As such, FlashGraph is now able to support a wide
-variety of data mining and machine learning algorithms. We address the entire
-data analysis framework with FlashGraph-ng. The current implementation of
-FlashGraph-ng has four main components:
+FlashX is a collection of big data analytics tools that perform data analytics
+in the form of graphs and matrices. They utilize solid-state drives (SSDs) to
+scale to large datasets in a single machine. It has five main components:
+SAFS, FlashGraph, FlashMatrix, FlashEigen and FlashR.
 
 SAFS
 ========
@@ -37,6 +34,14 @@ varieties of data mining and machine learning algorithms. For certain graph
 algorithms such as PageRank, which can be formulated as sparse matrix
 multiplication, FlashMatrix is able to significantly outperform FlashGraph.
 
+FlashEigen
+==========
+FlashEigen is an eigensolver that extends the
+[Anasazi](https://trilinos.org/packages/anasazi/) eigensolvers with FlashMatrix.
+It computes eigenvalues of billion-node graphs efficiently and pricisely
+in a single machine. As such, FlashEigen enables users to perform spectral
+analysis on very large graphs in a single machine.
+
 FlashR
 ===========